Tutorial: Inferring enhancer-GRNs from multiome data with SCENIC+

[ ]:
%matplotlib inline
import scenicplus
scenicplus.__version__

In this tutorial we describe the minimum steps to generate a SCENIC+ object and build e-GRNs. Detailed tutorials for exploring the results will be provided.

1. Create SCENIC+ object

For generating a SCENIC+ you will require: * scRNA-seq annData object (e.g. scanpy) * scATAC-seq cisTopic object * Pycistarget motif enrichment dictionary

[1]:
# Load functions
from scenicplus.scenicplus_class import SCENICPLUS, create_SCENICPLUS_object
from scenicplus.preprocessing.filtering import *

First we will load the scRNA-seq and the scATAC-seq data. We make sure that names match between them.

[2]:
# Load data
## ATAC - cisTopic object
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/10x_multiome_brain_cisTopicObject_noDBL.pkl', 'rb')
cistopic_obj = pickle.load(infile)
infile.close()
## Precomputed imputed data
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/DARs/Imputed_accessibility.pkl', 'rb')
imputed_acc_obj = pickle.load(infile)
infile.close()
## RNA - Create Anndata
from loomxpy.loomxpy import SCopeLoom
from pycisTopic.loom import *
import itertools
import anndata
path_to_loom = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/rna/seurat/10x_multiome_brain_Seurat.loom'
loom = SCopeLoom.read_loom(path_to_loom)
metadata = get_metadata(loom)
# Fix names
metadata['barcode'] = [x.split('-')[0] for x in metadata.index.tolist()]
metadata['barcode'] = metadata['barcode'] + '-1'
metadata.index = metadata['barcode'] + '-10x_multiome_brain'
expr_mat = loom.ex_mtx
expr_mat.index = metadata['barcode'] + '-10x_multiome_brain'
rna_anndata = anndata.AnnData(X=expr_mat)
rna_anndata.obs = metadata

If you have generated your cisTopic object with an old version of pycisTopic, it is possible that your region data was affected by a previous bug. You can fix it with the code below:

[3]:
# Fix region data (bug in old pycistopic versions)
from pycisTopic.utils import region_names_to_coordinates
fragment_matrix = cistopic_obj.fragment_matrix
binary_matrix = cistopic_obj.binary_matrix
region_data = region_names_to_coordinates(cistopic_obj.region_names)
region_data['Width'] = abs(region_data.End -region_data.Start).astype(np.int32)
region_data['cisTopic_nr_frag'] = np.array(
fragment_matrix.sum(axis=1)).flatten()
region_data['cisTopic_log_nr_frag'] = np.log10(
region_data['cisTopic_nr_frag'])
region_data['cisTopic_nr_acc'] = np.array(
binary_matrix.sum(axis=1)).flatten()
region_data['cisTopic_log_nr_acc'] = np.log10(
region_data['cisTopic_nr_acc'])
cistopic_obj.region_data = region_data

Next we load the motif enrichment results into a dictionary. We can load motif results from the different methods in pycistarget (e.g. cisTarget, DEM) and different region sets (e.g. topics, DARs, MACS bdgdiff peaks). In this tutorial we will use both cisTarget and DEM peaks from topics and DARs.

[4]:
# Load cistarget and DEM motif enrichment results
motif_enrichment_dict={}
import pickle
from pycistarget.motif_enrichment_dem import *
path = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistarget/'
infile = open(path +'topics/topic_cistarget_dict.pkl', 'rb')
motif_enrichment_dict['CTX_Topics_All'] = pickle.load(infile)
infile.close()
infile = open(path +'topics/topic_DEM_dict.pkl', 'rb')
motif_enrichment_dict['DEM_Topics_All'] = pickle.load(infile)
infile.close()
infile = open(path +'DARs/DARs_cistarget_dict.pkl', 'rb')
motif_enrichment_dict['CTX_DARs_All'] = pickle.load(infile)
infile.close()
infile = open(path +'DARs/DARs_DEM_dict.pkl', 'rb')
motif_enrichment_dict['DEM_DARs_All'] = pickle.load(infile)
infile.close()

Now we can create the SCENIC+ object:

[5]:
scplus_obj = create_SCENICPLUS_object(
        GEX_anndata = rna_anndata,
        cisTopic_obj = cistopic_obj,
        imputed_acc_obj = imputed_acc_obj,
        menr = motif_enrichment_dict,
        ACC_prefix = 'ACC_',
        GEX_prefix = 'GEX_',
        bc_transform_func = lambda x: x,
        normalize_imputed_acc = False)
[6]:
print(scplus_obj)
SCENIC+ object with n_cells x n_genes = 1736 x 26399 and n_cells x n_regions = 1736 x 422146
        metadata_regions:'Chromosome', 'Start', 'End', 'Width', 'cisTopic_nr_frag', 'cisTopic_log_nr_frag', 'cisTopic_nr_acc', 'cisTopic_log_nr_acc'
        metadata_cell:'GEX_VSN_cell_type', 'GEX_VSN_leiden_res0.3', 'GEX_VSN_leiden_res0.6', 'GEX_VSN_leiden_res0.9', 'GEX_VSN_leiden_res1.2', 'GEX_VSN_sample_id', 'GEX_Seurat_leiden_res0.6', 'GEX_Seurat_leiden_res1.2', 'GEX_Seurat_cell_type', 'GEX_barcode', 'ACC_cisTopic_nr_frag', 'ACC_cisTopic_log_nr_acc', 'ACC_barcode', 'ACC_VSN_leiden_res0.6', 'ACC_FRIP', 'ACC_TSS_enrichment', 'ACC_cisTopic_nr_acc', 'ACC_VSN_leiden_res0.9', 'ACC_VSN_RNA+ATAC_leiden_100_2', 'ACC_Dupl_rate', 'ACC_sample_id', 'ACC_pycisTopic_leiden_10_0.6', 'ACC_pycisTopic_leiden_10_1.2', 'ACC_Seurat_leiden_res0.6', 'ACC_Total_nr_frag', 'ACC_Total_nr_frag_in_regions', 'ACC_VSN_leiden_res1.2', 'ACC_VSN_cell_type', 'ACC_Unique_nr_frag_in_regions', 'ACC_Seurat_cell_type', 'ACC_Predicted_doublets_fragments', 'ACC_Dupl_nr_frag', 'ACC_cisTopic_log_nr_frag', 'ACC_VSN_leiden_res0.3', 'ACC_Log_total_nr_frag', 'ACC_Unique_nr_frag', 'ACC_Doublet_scores_fragments', 'ACC_VSN_sample_id', 'ACC_Log_unique_nr_frag', 'ACC_Seurat_leiden_res1.2', 'ACC_Seurat_RNA+ATAC_leiden_100_2', 'ACC_pycisTopic_ingest_cell_type', 'ACC_pycisTopic_harmony_cell_type', 'ACC_pycisTopic_bbknn_cell_type', 'ACC_pycisTopic_scanorama_cell_type', 'ACC_pycisTopic_cca_cell_type'
        menr:'CTX_Topics_All', 'DEM_Topics_All', 'CTX_DARs_All', 'DEM_DARs_All'
        dr_cell:'ACC_UMAP', 'ACC_tSNE', 'ACC_VSN_RNA+ATAC_UMAP', 'ACC_Seurat_RNA+ATAC_UMAP'

You can also filter low accessible regions and low expressed genes. This recommended to avoid getting false relationships with these regions and genes.

[7]:
filter_genes(scplus_obj, min_pct = 0.5)
filter_regions(scplus_obj, min_pct = 0.5)
2022-01-04 17:24:16,750 Preprocessing INFO     Going from 26399 genes to 20448 genes.
2022-01-04 17:24:50,192 Preprocessing INFO     Going from 422146 regions to 375805 regions.

2. Generate cistromes

The next step is to generate cistromes. By default, all targets assigned to a TF across the motif enrichment dictionaries will be taken, and overlapped with regions in the SCENIC+ object. However, it is possible to also subset for regions accessible in certain cell type as well, to generate cell type specific cistromes. This approach is described in the tutorial cistrome_pruning_advanced.ipynb.

[8]:
# Merge cistromes (all)
from scenicplus.cistromes import *
import time
start_time = time.time()
merge_cistromes(scplus_obj)
time = time.time()-start_time
print(time/60)
7.251678760846456

3. Optional: Assess TF-cistrome correlation

In addition, cistromes can be pruned based on the correlarion between the presence of a TF and the accessibility of the cistrome. SCENIC+ can be run without this pruning step, deriving eGRNs for each candidate TF, but the assesment of the TF-cistrome correlation can help to prioritize TFs and high confidence GRNs.

A. Score cistromes in cells

The first step is to score the cistromes in each cell. For this purpose, we will use AUCell, using cell rankings based on region accessibility.

[9]:
# Generate AUC ranking
import time
start_time = time.time()
ranking = make_rankings(scplus_obj)
time = time.time()-start_time
print(time/60)
2.333057375748952
[10]:
# Get cistrome enrichment
import time
start_time = time.time()
score_cistromes(scplus_obj,
                ranking,
                cistromes_key = 'Unfiltered',
                enrichment_type = 'region',
                auc_threshold = 0.05,
                normalize = False,
                n_cpu = 8)
time = time.time()-start_time
print(time/60)
4.658317136764526

B. Generate pseudobulks

Due to the amount of drop-outs, and the variability in cell types proportions, using directly the AUC cistrome matrix can result in noisy correlations. Here, we use pseudobulks, in which we sample a number of cells per cell type. In this example, we merge 5 cells per pseudobulk and generate 100 pseudobulks per cell type.

[11]:
import time
start_time = time.time()
generate_pseudobulks(scplus_obj,
                         variable = 'ACC_Seurat_cell_type',
                         signature_key = 'Unfiltered',
                         nr_cells = 5,
                         nr_pseudobulks = 100,
                         seed=555)
time = time.time()-start_time
print(time/60)
1.0019505302111307

C. Calculate correlation

Using the pseudobulk TF expression and cistrome AUC matrix we can now assess the correlation between a TF and potential target regions.

[12]:
import time
start_time = time.time()
TF_cistrome_correlation(scplus_obj,
                        variable = 'ACC_Seurat_cell_type',
                        signature_key = 'Unfiltered',
                        out_key = 'ACC_Seurat_cell_type_unfiltered')
time = time.time()-start_time
print(time/60)
0.021760455767313638

Let’s take a look to the TFs we find here:

[13]:
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.float_format', lambda x: '%.5f' % x)
display(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered'].sort_values('Rho', ascending=False))
TF Cistrome Rho P-value Adjusted_p-value
150 IKZF1 IKZF1_(6418r) 0.91915 0.00000 0.00000
650 IKZF1 IKZF1_extended_(6418r) 0.91915 0.00000 0.00000
310 RUNX1 RUNX1_(507r) 0.90647 0.00000 0.00000
831 RUNX1 RUNX1_extended_(507r) 0.90647 0.00000 0.00000
585 FLI1 FLI1_extended_(12449r) 0.89567 0.00000 0.00000
104 FLI1 FLI1_(11724r) 0.89503 0.00000 0.00000
200 MEF2C MEF2C_(14677r) 0.85657 0.00000 0.00000
706 MEF2C MEF2C_extended_(15468r) 0.85115 0.00000 0.00000
158 IRF8 IRF8_(6862r) 0.83591 0.00000 0.00000
658 IRF8 IRF8_extended_(6862r) 0.83591 0.00000 0.00000
88 EMX2 EMX2_(5529r) 0.80001 0.00000 0.00000
569 EMX2 EMX2_extended_(6882r) 0.78655 0.00000 0.00000
858 SOX10 SOX10_extended_(9242r) 0.78392 0.00000 0.00000
331 SOX10 SOX10_(9242r) 0.78392 0.00000 0.00000
202 MEIS1 MEIS1_(30r) 0.77788 0.00000 0.00000
708 MEIS1 MEIS1_extended_(64r) 0.76204 0.00000 0.00000
11 ARNTL2 ARNTL2_(86r) 0.75733 0.00000 0.00000
486 ARNTL2 ARNTL2_extended_(86r) 0.75733 0.00000 0.00000
52 CREB5 CREB5_(92r) 0.74138 0.00000 0.00000
530 CREB5 CREB5_extended_(92r) 0.74138 0.00000 0.00000
770 PAX6 PAX6_extended_(4540r) 0.73547 0.00000 0.00000
815 REL REL_extended_(1512r) 0.72930 0.00000 0.00000
227 NEUROD2 NEUROD2_(1236r) 0.72845 0.00000 0.00000
595 FOXN2 FOXN2_extended_(836r) 0.71751 0.00000 0.00000
820 RFX1 RFX1_extended_(7457r) 0.67600 0.00000 0.00000
302 RFX1 RFX1_(6259r) 0.67498 0.00000 0.00000
262 PAX6 PAX6_(581r) 0.65941 0.00000 0.00000
850 SMARCA4 SMARCA4_extended_(12314r) 0.64532 0.00000 0.00000
324 SMARCA4 SMARCA4_(12314r) 0.64532 0.00000 0.00000
726 MYEF2 MYEF2_extended_(3919r) 0.62438 0.00000 0.00000
560 EGR4 EGR4_extended_(6902r) 0.62326 0.00000 0.00000
36 CCDC25 CCDC25_(2025r) 0.61721 0.00000 0.00000
512 CCDC25 CCDC25_extended_(2025r) 0.61721 0.00000 0.00000
79 EGR4 EGR4_(4237r) 0.60747 0.00000 0.00000
716 MTA3 MTA3_extended_(5767r) 0.60682 0.00000 0.00000
209 MTA3 MTA3_(5767r) 0.60682 0.00000 0.00000
343 SOX8 SOX8_(7356r) 0.59894 0.00000 0.00000
871 SOX8 SOX8_extended_(8129r) 0.59815 0.00000 0.00000
10 ARNTL ARNTL_(86r) 0.59148 0.00000 0.00000
487 ARNTL ARNTL_extended_(86r) 0.59148 0.00000 0.00000
803 PRRX1 PRRX1_extended_(7009r) 0.58549 0.00000 0.00000
287 PRRX1 PRRX1_(6328r) 0.57687 0.00000 0.00000
739 NFE2L1 NFE2L1_extended_(7150r) 0.57328 0.00000 0.00000
184 LHX2 LHX2_(7088r) 0.57308 0.00000 0.00000
182 LARP1 LARP1_(552r) 0.57113 0.00000 0.00000
687 LARP1 LARP1_extended_(552r) 0.57113 0.00000 0.00000
478 AHR AHR_extended_(441r) 0.56964 0.00000 0.00000
3 AHR AHR_(441r) 0.56964 0.00000 0.00000
232 NFE2L1 NFE2L1_(6690r) 0.56898 0.00000 0.00000
96 ETS2 ETS2_(8939r) 0.56829 0.00000 0.00000
577 ETS2 ETS2_extended_(9030r) 0.56826 0.00000 0.00000
1014 ZNF821 ZNF821_extended_(446r) 0.56454 0.00000 0.00000
369 TAL1 TAL1_(5967r) 0.55784 0.00000 0.00000
503 BCL6 BCL6_extended_(455r) 0.55630 0.00000 0.00000
27 BCL6 BCL6_(455r) 0.55630 0.00000 0.00000
682 KLF6 KLF6_extended_(2680r) 0.55533 0.00000 0.00000
178 KLF6 KLF6_(2680r) 0.55533 0.00000 0.00000
826 RFX8 RFX8_extended_(4255r) 0.54445 0.00000 0.00000
689 LHX2 LHX2_extended_(9340r) 0.54426 0.00000 0.00000
447 ZNF382 ZNF382_(112r) 0.53483 0.00000 0.00000
986 ZNF382 ZNF382_extended_(112r) 0.53483 0.00000 0.00000
38 CEBPA CEBPA_(101r) 0.53361 0.00000 0.00000
514 CEBPA CEBPA_extended_(101r) 0.53361 0.00000 0.00000
774 PBX3 PBX3_extended_(2962r) 0.53326 0.00000 0.00000
266 PBX3 PBX3_(2962r) 0.53326 0.00000 0.00000
825 RFX7 RFX7_extended_(4837r) 0.53283 0.00000 0.00000
4 AHRR AHRR_(348r) 0.52820 0.00000 0.00000
479 AHRR AHRR_extended_(348r) 0.52820 0.00000 0.00000
742 NFIA NFIA_extended_(10467r) 0.51856 0.00000 0.00000
235 NFIA NFIA_(10467r) 0.51856 0.00000 0.00000
351 SPI1 SPI1_(12054r) 0.51377 0.00000 0.00000
457 ZNF536 ZNF536_(1607r) 0.51254 0.00000 0.00000
997 ZNF536 ZNF536_extended_(1607r) 0.51254 0.00000 0.00000
484 ARNT2 ARNT2_extended_(481r) 0.51109 0.00000 0.00000
9 ARNT2 ARNT2_(481r) 0.51109 0.00000 0.00000
219 MYEF2 MYEF2_(287r) 0.51014 0.00000 0.00000
880 SPI1 SPI1_extended_(13968r) 0.50402 0.00000 0.00000
256 OLIG2 OLIG2_(7201r) 0.50288 0.00000 0.00000
764 OLIG2 OLIG2_extended_(7201r) 0.50288 0.00000 0.00000
562 ELF1 ELF1_extended_(13965r) 0.49355 0.00000 0.00000
81 ELF1 ELF1_(13431r) 0.49258 0.00000 0.00000
308 RFXAP RFXAP_(4793r) 0.49246 0.00000 0.00000
828 RFXAP RFXAP_extended_(4793r) 0.49246 0.00000 0.00000
101 ETV6 ETV6_(6366r) 0.48592 0.00000 0.00000
853 SMARCC2 SMARCC2_extended_(876r) 0.48340 0.00000 0.00000
327 SMARCC2 SMARCC2_(876r) 0.48340 0.00000 0.00000
490 ATF1 ATF1_extended_(1005r) 0.48174 0.00000 0.00000
14 ATF1 ATF1_(1005r) 0.48174 0.00000 0.00000
754 NPAS2 NPAS2_extended_(86r) 0.47330 0.00000 0.00000
247 NPAS2 NPAS2_(86r) 0.47330 0.00000 0.00000
511 CBX3 CBX3_extended_(6920r) 0.46862 0.00000 0.00000
35 CBX3 CBX3_(6920r) 0.46862 0.00000 0.00000
582 ETV6 ETV6_extended_(7207r) 0.45655 0.00000 0.00000
617 HBP1 HBP1_extended_(4730r) 0.45618 0.00000 0.00000
126 HBP1 HBP1_(3641r) 0.45393 0.00000 0.00000
976 ZNF250 ZNF250_extended_(556r) 0.44956 0.00000 0.00000
437 ZNF250 ZNF250_(552r) 0.44840 0.00000 0.00000
667 KDM4B KDM4B_extended_(8r) 0.43758 0.00000 0.00000
15 ATF2 ATF2_(3151r) 0.43729 0.00000 0.00000
491 ATF2 ATF2_extended_(3151r) 0.43729 0.00000 0.00000
46 CLOCK CLOCK_(86r) 0.43667 0.00000 0.00000
523 CLOCK CLOCK_extended_(86r) 0.43667 0.00000 0.00000
186 LUZP2 LUZP2_(552r) 0.43562 0.00000 0.00000
691 LUZP2 LUZP2_extended_(552r) 0.43562 0.00000 0.00000
876 SP3 SP3_extended_(9224r) 0.43501 0.00000 0.00000
714 MNT MNT_extended_(86r) 0.43419 0.00000 0.00000
207 MNT MNT_(86r) 0.43419 0.00000 0.00000
398 USF2 USF2_(86r) 0.43202 0.00000 0.00000
932 USF2 USF2_extended_(86r) 0.43202 0.00000 0.00000
666 KDM4A KDM4A_extended_(8r) 0.42996 0.00000 0.00000
743 NFIB NFIB_extended_(8840r) 0.42645 0.00000 0.00000
236 NFIB NFIB_(8840r) 0.42645 0.00000 0.00000
838 SALL3 SALL3_extended_(236r) 0.42634 0.00000 0.00000
678 KLF2 KLF2_extended_(869r) 0.42121 0.00000 0.00000
174 KLF2 KLF2_(869r) 0.42121 0.00000 0.00000
877 SP4 SP4_extended_(8857r) 0.41944 0.00000 0.00000
347 SP3 SP3_(7086r) 0.41346 0.00000 0.00000
415 ZBTB20 ZBTB20_(142r) 0.41146 0.00000 0.00000
948 ZBTB20 ZBTB20_extended_(142r) 0.41146 0.00000 0.00000
1011 ZNF736 ZNF736_extended_(685r) 0.41029 0.00000 0.00000
471 ZNF736 ZNF736_(685r) 0.41029 0.00000 0.00000
445 ZNF341 ZNF341_(2412r) 0.40950 0.00000 0.00000
983 ZNF341 ZNF341_extended_(2412r) 0.40950 0.00000 0.00000
668 KDM4C KDM4C_extended_(8r) 0.40902 0.00000 0.00000
348 SP4 SP4_(6696r) 0.40222 0.00000 0.00000
497 ATF7 ATF7_extended_(139r) 0.39501 0.00000 0.00000
822 RFX3 RFX3_extended_(6040r) 0.39401 0.00000 0.00000
516 CEBPD CEBPD_extended_(475r) 0.39238 0.00000 0.00000
40 CEBPD CEBPD_(475r) 0.39238 0.00000 0.00000
304 RFX3 RFX3_(4970r) 0.39137 0.00000 0.00000
433 ZNF148 ZNF148_(3124r) 0.38756 0.00000 0.00000
971 ZNF148 ZNF148_extended_(3124r) 0.38756 0.00000 0.00000
282 POU3F4 POU3F4_(1216r) 0.38535 0.00000 0.00000
56 CUX1 CUX1_(3292r) 0.38511 0.00000 0.00000
536 CUX1 CUX1_extended_(3292r) 0.38511 0.00000 0.00000
206 MLXIPL MLXIPL_(86r) 0.38350 0.00000 0.00000
713 MLXIPL MLXIPL_extended_(86r) 0.38350 0.00000 0.00000
855 SNAPC4 SNAPC4_extended_(956r) 0.38249 0.00000 0.00000
21 ATF7 ATF7_(108r) 0.37613 0.00000 0.00000
734 NEUROD2 NEUROD2_extended_(1960r) 0.37108 0.00000 0.00000
250 NR2E1 NR2E1_(3366r) 0.37015 0.00000 0.00000
396 UQCRB UQCRB_(64r) 0.36999 0.00000 0.00000
930 UQCRB UQCRB_extended_(64r) 0.36999 0.00000 0.00000
566 ELK3 ELK3_extended_(8266r) 0.36740 0.00000 0.00000
715 MSX1 MSX1_extended_(9402r) 0.36481 0.00000 0.00000
758 NR2E1 NR2E1_extended_(3758r) 0.36420 0.00000 0.00000
85 ELK3 ELK3_(7672r) 0.35844 0.00000 0.00000
152 ILF2 ILF2_(129r) 0.35742 0.00000 0.00000
652 ILF2 ILF2_extended_(129r) 0.35742 0.00000 0.00000
952 ZBTB37 ZBTB37_extended_(126r) 0.35490 0.00000 0.00000
418 ZBTB37 ZBTB37_(126r) 0.35490 0.00000 0.00000
208 MSX1 MSX1_(8145r) 0.35281 0.00000 0.00000
2 AGGF1 AGGF1_(2933r) 0.34778 0.00000 0.00000
477 AGGF1 AGGF1_extended_(2933r) 0.34778 0.00000 0.00000
936 VSX1 VSX1_extended_(5820r) 0.34606 0.00000 0.00000
402 VSX1 VSX1_(5820r) 0.34606 0.00000 0.00000
485 ARNT ARNT_extended_(2151r) 0.34487 0.00000 0.00000
8 ARNT ARNT_(2151r) 0.34487 0.00000 0.00000
175 KLF3 KLF3_(2710r) 0.34440 0.00000 0.00000
977 ZNF263 ZNF263_extended_(4118r) 0.34422 0.00000 0.00000
438 ZNF263 ZNF263_(4118r) 0.34422 0.00000 0.00000
679 KLF3 KLF3_extended_(2749r) 0.34276 0.00000 0.00000
198 MEF2A MEF2A_(17250r) 0.33771 0.00000 0.00000
669 KDM4D KDM4D_extended_(8r) 0.33649 0.00000 0.00000
153 ING4 ING4_(352r) 0.33589 0.00000 0.00000
653 ING4 ING4_extended_(352r) 0.33589 0.00000 0.00000
312 RXRA RXRA_(9141r) 0.33282 0.00000 0.00000
833 RXRA RXRA_extended_(9141r) 0.33282 0.00000 0.00000
704 MEF2A MEF2A_extended_(17929r) 0.33153 0.00000 0.00000
372 TCF12 TCF12_(14335r) 0.32412 0.00000 0.00000
469 ZNF71 ZNF71_(311r) 0.32408 0.00000 0.00000
1009 ZNF71 ZNF71_extended_(311r) 0.32408 0.00000 0.00000
258 OTX1 OTX1_(4081r) 0.32386 0.00000 0.00000
766 OTX1 OTX1_extended_(4081r) 0.32386 0.00000 0.00000
963 ZFP64 ZFP64_extended_(2710r) 0.32344 0.00000 0.00000
999 ZNF543 ZNF543_extended_(1550r) 0.32291 0.00000 0.00000
459 ZNF543 ZNF543_(1550r) 0.32291 0.00000 0.00000
857 SOHLH2 SOHLH2_extended_(345r) 0.31969 0.00000 0.00000
285 PRDM1 PRDM1_(1931r) 0.31889 0.00000 0.00000
801 PRDM1 PRDM1_extended_(1931r) 0.31889 0.00000 0.00000
865 SOX2 SOX2_extended_(7976r) 0.31557 0.00000 0.00000
336 SOX2 SOX2_(7976r) 0.31557 0.00000 0.00000
749 NHLH2 NHLH2_extended_(2777r) 0.31482 0.00000 0.00000
242 NHLH2 NHLH2_(2777r) 0.31482 0.00000 0.00000
363 STAT6 STAT6_(2649r) 0.31476 0.00000 0.00000
892 STAT6 STAT6_extended_(2649r) 0.31476 0.00000 0.00000
559 EGR3 EGR3_extended_(11410r) 0.31457 0.00000 0.00000
1012 ZNF740 ZNF740_extended_(1970r) 0.31007 0.00000 0.00000
78 EGR3 EGR3_(7095r) 0.30646 0.00000 0.00000
169 KLF11 KLF11_(1520r) 0.30507 0.00000 0.00000
673 KLF11 KLF11_extended_(1520r) 0.30507 0.00000 0.00000
564 ELF4 ELF4_extended_(8921r) 0.30229 0.00000 0.00000
83 ELF4 ELF4_(7253r) 0.30061 0.00000 0.00000
540 DBX2 DBX2_extended_(3565r) 0.29937 0.00000 0.00000
709 MEIS2 MEIS2_extended_(1675r) 0.29895 0.00000 0.00000
901 TCF12 TCF12_extended_(15572r) 0.29634 0.00000 0.00000
944 ZBTB11 ZBTB11_extended_(394r) 0.29585 0.00000 0.00000
410 ZBTB11 ZBTB11_(394r) 0.29585 0.00000 0.00000
535 CTCF CTCF_extended_(9207r) 0.29269 0.00000 0.00000
292 RAD21 RAD21_(5535r) 0.28986 0.00000 0.00000
808 RAD21 RAD21_extended_(5535r) 0.28986 0.00000 0.00000
928 TWIST2 TWIST2_extended_(1045r) 0.28982 0.00000 0.00000
981 ZNF282 ZNF282_extended_(82r) 0.28868 0.00000 0.00000
442 ZNF282 ZNF282_(82r) 0.28868 0.00000 0.00000
903 TCF4 TCF4_extended_(5874r) 0.28829 0.00000 0.00000
317 SETDB1 SETDB1_(3656r) 0.28808 0.00000 0.00000
841 SETDB1 SETDB1_extended_(3656r) 0.28808 0.00000 0.00000
856 SNRPB2 SNRPB2_extended_(63r) 0.28722 0.00000 0.00000
329 SNRPB2 SNRPB2_(63r) 0.28722 0.00000 0.00000
1019 ZXDB ZXDB_extended_(124r) 0.28674 0.00000 0.00000
30 BHLHE40 BHLHE40_(86r) 0.28630 0.00000 0.00000
326 SMARCC1 SMARCC1_(12697r) 0.28565 0.00000 0.00000
852 SMARCC1 SMARCC1_extended_(12697r) 0.28565 0.00000 0.00000
45 CHURC1 CHURC1_(2084r) 0.28541 0.00000 0.00000
521 CHURC1 CHURC1_extended_(2084r) 0.28541 0.00000 0.00000
32 BRCA1 BRCA1_(120r) 0.28494 0.00000 0.00000
508 BRCA1 BRCA1_extended_(120r) 0.28494 0.00000 0.00000
731 NELFB NELFB_extended_(3109r) 0.28350 0.00000 0.00000
224 NELFB NELFB_(3109r) 0.28350 0.00000 0.00000
203 MEIS3 MEIS3_(684r) 0.28340 0.00000 0.00000
873 SP100 SP100_extended_(251r) 0.28215 0.00000 0.00000
851 SMARCB1 SMARCB1_extended_(5767r) 0.27961 0.00000 0.00000
325 SMARCB1 SMARCB1_(5767r) 0.27961 0.00000 0.00000
298 RCOR1 RCOR1_(12358r) 0.27788 0.00000 0.00000
814 RCOR1 RCOR1_extended_(12358r) 0.27788 0.00000 0.00000
440 ZNF274 ZNF274_(687r) 0.27413 0.00000 0.00000
979 ZNF274 ZNF274_extended_(687r) 0.27413 0.00000 0.00000
425 ZEB2 ZEB2_(877r) 0.27357 0.00000 0.00000
959 ZEB2 ZEB2_extended_(877r) 0.27357 0.00000 0.00000
699 MAZ MAZ_extended_(11003r) 0.27271 0.00000 0.00000
194 MAZ MAZ_(11003r) 0.27271 0.00000 0.00000
34 BRF2 BRF2_(3325r) 0.27062 0.00000 0.00000
510 BRF2 BRF2_extended_(3325r) 0.27062 0.00000 0.00000
210 MTF1 MTF1_(1826r) 0.27052 0.00000 0.00000
717 MTF1 MTF1_extended_(1826r) 0.27052 0.00000 0.00000
164 JUND JUND_(17492r) 0.27030 0.00000 0.00000
664 JUND JUND_extended_(18288r) 0.26845 0.00000 0.00000
374 TCF4 TCF4_(3842r) 0.26807 0.00000 0.00000
962 ZFP2 ZFP2_extended_(52r) 0.26759 0.00000 0.00000
55 CTCF CTCF_(8149r) 0.26658 0.00000 0.00000
922 TPPP TPPP_extended_(3109r) 0.26635 0.00000 0.00000
390 TPPP TPPP_(3109r) 0.26635 0.00000 0.00000
794 POU3F4 POU3F4_extended_(1685r) 0.26571 0.00000 0.00000
701 MCTP2 MCTP2_extended_(2153r) 0.26339 0.00000 0.00000
195 MCTP2 MCTP2_(2153r) 0.26339 0.00000 0.00000
522 CIC CIC_extended_(402r) 0.25952 0.00000 0.00000
760 NRF1 NRF1_extended_(7694r) 0.25530 0.00000 0.00000
452 ZNF454 ZNF454_(692r) 0.25429 0.00000 0.00000
992 ZNF454 ZNF454_extended_(692r) 0.25429 0.00000 0.00000
278 POU2F2 POU2F2_(638r) 0.25283 0.00000 0.00000
252 NRF1 NRF1_(7141r) 0.25043 0.00000 0.00000
854 SMC3 SMC3_extended_(3926r) 0.24873 0.00000 0.00000
328 SMC3 SMC3_(3926r) 0.24873 0.00000 0.00000
98 ETV3 ETV3_(3310r) 0.24196 0.00000 0.00000
26 BCL11A BCL11A_(10556r) 0.24017 0.00000 0.00000
502 BCL11A BCL11A_extended_(10556r) 0.24017 0.00000 0.00000
470 ZNF732 ZNF732_(59r) 0.23947 0.00000 0.00000
1010 ZNF732 ZNF732_extended_(59r) 0.23947 0.00000 0.00000
201 MEF2D MEF2D_(5093r) 0.23931 0.00000 0.00000
707 MEF2D MEF2D_extended_(5929r) 0.23213 0.00000 0.00000
284 POU6F2 POU6F2_(5943r) 0.23162 0.00000 0.00000
796 POU6F2 POU6F2_extended_(5961r) 0.23129 0.00000 0.00000
969 ZNF134 ZNF134_extended_(3464r) 0.22578 0.00000 0.00000
431 ZNF134 ZNF134_(3464r) 0.22578 0.00000 0.00000
47 CPSF4 CPSF4_(2476r) 0.22459 0.00000 0.00000
524 CPSF4 CPSF4_extended_(2476r) 0.22459 0.00000 0.00000
882 SRBD1 SRBD1_extended_(287r) 0.22370 0.00000 0.00000
353 SRBD1 SRBD1_(287r) 0.22370 0.00000 0.00000
444 ZNF316 ZNF316_(338r) 0.22360 0.00000 0.00000
982 ZNF316 ZNF316_extended_(338r) 0.22360 0.00000 0.00000
517 CEBPG CEBPG_extended_(96r) 0.22088 0.00000 0.00000
41 CEBPG CEBPG_(96r) 0.22088 0.00000 0.00000
710 MEIS3 MEIS3_extended_(720r) 0.21974 0.00000 0.00000
405 XRCC4 XRCC4_(528r) 0.21907 0.00000 0.00000
939 XRCC4 XRCC4_extended_(528r) 0.21907 0.00000 0.00000
453 ZNF467 ZNF467_(2412r) 0.21834 0.00000 0.00000
993 ZNF467 ZNF467_extended_(2412r) 0.21834 0.00000 0.00000
898 TAL1 TAL1_extended_(8409r) 0.21401 0.00000 0.00000
239 NFYA NFYA_(2536r) 0.21293 0.00000 0.00000
579 ETV3 ETV3_extended_(4044r) 0.21273 0.00000 0.00000
817 RELB RELB_extended_(1512r) 0.21029 0.00000 0.00000
60 DBX2 DBX2_(581r) 0.21020 0.00000 0.00000
746 NFYA NFYA_extended_(2624r) 0.20570 0.00000 0.00000
693 MAFB MAFB_extended_(7092r) 0.20316 0.00000 0.00000
506 BHLHE40 BHLHE40_extended_(305r) 0.20189 0.00000 0.00000
696 MAFK MAFK_extended_(9603r) 0.20008 0.00000 0.00000
698 MAX MAX_extended_(3847r) 0.19988 0.00000 0.00000
193 MAX MAX_(3847r) 0.19988 0.00000 0.00000
191 MAFK MAFK_(8903r) 0.19796 0.00000 0.00000
473 ZNF823 ZNF823_(1823r) 0.19459 0.00000 0.00000
1015 ZNF823 ZNF823_extended_(1823r) 0.19459 0.00000 0.00000
881 SPIB SPIB_extended_(10746r) 0.19165 0.00000 0.00000
352 SPIB SPIB_(10746r) 0.19165 0.00000 0.00000
190 MAFG MAFG_(7092r) 0.18265 0.00000 0.00000
695 MAFG MAFG_extended_(7092r) 0.18265 0.00000 0.00000
17 ATF4 ATF4_(227r) 0.18135 0.00000 0.00000
493 ATF4 ATF4_extended_(227r) 0.18135 0.00000 0.00000
614 GTF3A GTF3A_extended_(124r) 0.18022 0.00000 0.00000
253 NUP107 NUP107_(532r) 0.17671 0.00000 0.00000
761 NUP107 NUP107_extended_(532r) 0.17671 0.00000 0.00000
100 ETV5 ETV5_(8138r) 0.17284 0.00000 0.00000
581 ETV5 ETV5_extended_(8138r) 0.17284 0.00000 0.00000
991 ZNF444 ZNF444_extended_(1550r) 0.17083 0.00000 0.00000
451 ZNF444 ZNF444_(1550r) 0.17083 0.00000 0.00000
275 POLR3G POLR3G_(563r) 0.16966 0.00000 0.00000
787 POLR3G POLR3G_extended_(563r) 0.16966 0.00000 0.00000
188 MAFB MAFB_(6690r) 0.16820 0.00000 0.00000
288 PRRX2 PRRX2_(7204r) 0.16807 0.00000 0.00000
804 PRRX2 PRRX2_extended_(7313r) 0.16787 0.00000 0.00000
115 FOXP1 FOXP1_(1536r) 0.16681 0.00000 0.00000
7 ARID3A ARID3A_(12437r) 0.16531 0.00000 0.00000
789 POU2F1 POU2F1_extended_(6248r) 0.16365 0.00000 0.00000
907 TEAD1 TEAD1_extended_(3109r) 0.15994 0.00000 0.00000
377 TEAD1 TEAD1_(3109r) 0.15994 0.00000 0.00000
280 POU3F2 POU3F2_(3107r) 0.15993 0.00000 0.00000
601 FOXP1 FOXP1_extended_(1691r) 0.15895 0.00000 0.00000
792 POU3F2 POU3F2_extended_(3197r) 0.15480 0.00000 0.00000
435 ZNF234 ZNF234_(85r) 0.15396 0.00000 0.00000
974 ZNF234 ZNF234_extended_(85r) 0.15396 0.00000 0.00000
515 CEBPB CEBPB_extended_(10863r) 0.15389 0.00000 0.00000
39 CEBPB CEBPB_(10810r) 0.15371 0.00000 0.00000
528 CREB3L2 CREB3L2_extended_(178r) 0.15238 0.00000 0.00000
155 IRF1 IRF1_(9396r) 0.15147 0.00000 0.00000
655 IRF1 IRF1_extended_(9396r) 0.15147 0.00000 0.00000
973 ZNF202 ZNF202_extended_(1970r) 0.14685 0.00000 0.00000
454 ZNF48 ZNF48_(311r) 0.14541 0.00000 0.00000
995 ZNF48 ZNF48_extended_(311r) 0.14541 0.00000 0.00000
426 ZFHX2 ZFHX2_(3510r) 0.14245 0.00000 0.00000
960 ZFHX2 ZFHX2_extended_(3510r) 0.14245 0.00000 0.00000
216 MYBL2 MYBL2_(34r) 0.14215 0.00000 0.00000
392 TRIM69 TRIM69_(25r) 0.14213 0.00000 0.00000
924 TRIM69 TRIM69_extended_(25r) 0.14213 0.00000 0.00000
824 RFX5 RFX5_extended_(9400r) 0.13924 0.00000 0.00000
370 TBL1XR1 TBL1XR1_(8264r) 0.13838 0.00000 0.00000
899 TBL1XR1 TBL1XR1_extended_(8264r) 0.13838 0.00000 0.00000
306 RFX5 RFX5_(8395r) 0.13711 0.00000 0.00000
220 MZF1 MZF1_(4946r) 0.13672 0.00000 0.00000
727 MZF1 MZF1_extended_(4946r) 0.13672 0.00000 0.00000
692 MAF MAF_extended_(7224r) 0.13430 0.00000 0.00000
143 HMGA2 HMGA2_(576r) 0.13335 0.00000 0.00000
636 HMGA2 HMGA2_extended_(576r) 0.13335 0.00000 0.00000
43 CHD1 CHD1_(7222r) 0.13149 0.00000 0.00000
519 CHD1 CHD1_extended_(7222r) 0.13149 0.00000 0.00000
168 KLF10 KLF10_(2366r) 0.13065 0.00000 0.00000
672 KLF10 KLF10_extended_(2366r) 0.13065 0.00000 0.00000
260 PATZ1 PATZ1_(2074r) 0.12819 0.00000 0.00000
768 PATZ1 PATZ1_extended_(2074r) 0.12819 0.00000 0.00000
945 ZBTB14 ZBTB14_extended_(7936r) 0.12619 0.00000 0.00000
411 ZBTB14 ZBTB14_(7936r) 0.12619 0.00000 0.00000
187 MAF MAF_(7092r) 0.12556 0.00000 0.00000
277 POU2F1 POU2F1_(3189r) 0.12412 0.00000 0.00000
213 MXI1 MXI1_(4449r) 0.12139 0.00000 0.00000
720 MXI1 MXI1_extended_(4449r) 0.12139 0.00000 0.00000
76 EGR1 EGR1_(11227r) 0.11974 0.00000 0.00000
376 TCF7L2 TCF7L2_(2373r) 0.11837 0.00000 0.00000
649 ID2 ID2_extended_(86r) 0.11594 0.00000 0.00001
571 EP300 EP300_extended_(20639r) 0.11543 0.00000 0.00001
90 EP300 EP300_(20639r) 0.11543 0.00000 0.00001
810 RARB RARB_extended_(1596r) 0.11420 0.00000 0.00001
294 RARB RARB_(1596r) 0.11420 0.00000 0.00001
482 ARID3A ARID3A_extended_(14486r) 0.11215 0.00001 0.00001
455 ZNF485 ZNF485_(1550r) 0.11000 0.00001 0.00002
994 ZNF485 ZNF485_extended_(1550r) 0.11000 0.00001 0.00002
677 KLF16 KLF16_extended_(913r) 0.10923 0.00001 0.00002
173 KLF16 KLF16_(767r) 0.10815 0.00001 0.00002
391 TRIM28 TRIM28_(12060r) 0.10750 0.00002 0.00002
923 TRIM28 TRIM28_extended_(12060r) 0.10750 0.00002 0.00002
557 EGR1 EGR1_extended_(13127r) 0.10623 0.00002 0.00003
618 HCFC1 HCFC1_extended_(5131r) 0.10608 0.00002 0.00003
127 HCFC1 HCFC1_(5131r) 0.10608 0.00002 0.00003
906 TCF7L2 TCF7L2_extended_(2539r) 0.10275 0.00004 0.00006
222 NANOS1 NANOS1_(707r) 0.09929 0.00007 0.00010
729 NANOS1 NANOS1_extended_(707r) 0.09929 0.00007 0.00010
77 EGR2 EGR2_(11261r) 0.09730 0.00010 0.00014
558 EGR2 EGR2_extended_(13538r) 0.09297 0.00020 0.00028
646 HSF4 HSF4_extended_(225r) 0.09206 0.00023 0.00032
627 HESX1 HESX1_extended_(5137r) 0.09193 0.00023 0.00033
135 HESX1 HESX1_(5137r) 0.09193 0.00023 0.00033
1018 ZXDA ZXDA_extended_(124r) 0.08988 0.00032 0.00045
548 DUXB DUXB_extended_(44r) 0.08077 0.00122 0.00167
619 HDAC1 HDAC1_extended_(528r) 0.07903 0.00156 0.00212
128 HDAC1 HDAC1_(528r) 0.07903 0.00156 0.00212
532 CREBZF CREBZF_extended_(4r) 0.07379 0.00314 0.00424
603 FOXP4 FOXP4_extended_(1691r) 0.07180 0.00406 0.00547
144 HMX1 HMX1_(4358r) 0.07067 0.00468 0.00625
640 HMX1 HMX1_extended_(4358r) 0.07067 0.00468 0.00625
609 GPANK1 GPANK1_extended_(1128r) 0.06909 0.00570 0.00757
120 GPANK1 GPANK1_(1128r) 0.06909 0.00570 0.00757
555 EBF1 EBF1_extended_(7268r) 0.06799 0.00651 0.00861
74 EBF1 EBF1_(7268r) 0.06799 0.00651 0.00861
403 WRNIP1 WRNIP1_(829r) 0.06783 0.00664 0.00876
937 WRNIP1 WRNIP1_extended_(829r) 0.06783 0.00664 0.00876
644 HSF1 HSF1_extended_(225r) 0.06315 0.01152 0.01503
972 ZNF165 ZNF165_extended_(167r) 0.06279 0.01201 0.01562
434 ZNF165 ZNF165_(167r) 0.06279 0.01201 0.01562
874 SP1 SP1_extended_(11230r) 0.06262 0.01224 0.01590
988 ZNF3 ZNF3_extended_(522r) 0.05878 0.01869 0.02410
443 ZNF3 ZNF3_(522r) 0.05878 0.01869 0.02410
890 STAT5A STAT5A_extended_(6247r) 0.05573 0.02581 0.03294
361 STAT5A STAT5A_(6247r) 0.05573 0.02581 0.03294
279 POU3F1 POU3F1_(4230r) 0.05547 0.02650 0.03365
791 POU3F1 POU3F1_extended_(4230r) 0.05547 0.02650 0.03365
345 SP1 SP1_(8854r) 0.05537 0.02677 0.03396
725 MYCN MYCN_extended_(2485r) 0.05432 0.02980 0.03770
218 MYCN MYCN_(2485r) 0.05432 0.02980 0.03770
446 ZNF354A ZNF354A_(142r) 0.05187 0.03802 0.04786
984 ZNF354A ZNF354A_extended_(142r) 0.05187 0.03802 0.04786
141 HIST1H2BN HIST1H2BN_(96r) 0.05057 0.04311 0.05387
633 HIST1H2BN HIST1H2BN_extended_(96r) 0.05057 0.04311 0.05387
110 FOXM1 FOXM1_(8310r) 0.04965 0.04708 0.05862
381 TFAP4 TFAP4_(7514r) 0.04957 0.04741 0.05888
911 TFAP4 TFAP4_extended_(7514r) 0.04957 0.04741 0.05888
529 CREB3L4 CREB3L4_extended_(202r) 0.04952 0.04765 0.05904
51 CREB3L4 CREB3L4_(202r) 0.04952 0.04765 0.05904
337 SOX21 SOX21_(4668r) 0.04947 0.04788 0.05926
461 ZNF580 ZNF580_(66r) 0.04943 0.04805 0.05932
1001 ZNF580 ZNF580_extended_(66r) 0.04943 0.04805 0.05932
594 FOXM1 FOXM1_extended_(8340r) 0.04925 0.04886 0.06017
570 ENO1 ENO1_extended_(86r) 0.04910 0.04957 0.06090
89 ENO1 ENO1_(86r) 0.04910 0.04957 0.06090
176 KLF4 KLF4_(5365r) 0.04896 0.05025 0.06144
680 KLF4 KLF4_extended_(5365r) 0.04896 0.05025 0.06144
806 PURA PURA_extended_(8479r) 0.04877 0.05111 0.06227
290 PURA PURA_(8479r) 0.04877 0.05111 0.06227
389 THRB THRB_(1596r) 0.04854 0.05225 0.06350
921 THRB THRB_extended_(1596r) 0.04854 0.05225 0.06350
875 SP2 SP2_extended_(9883r) 0.04852 0.05231 0.06351
606 GFI1B GFI1B_extended_(28r) 0.04836 0.05310 0.06439
319 SIN3A SIN3A_(9510r) 0.04788 0.05551 0.06707
843 SIN3A SIN3A_extended_(9510r) 0.04788 0.05551 0.06707
62 DIABLO DIABLO_(844r) 0.04411 0.07778 0.09289
542 DIABLO DIABLO_extended_(844r) 0.04411 0.07778 0.09289
864 SOX21 SOX21_extended_(6425r) 0.04324 0.08382 0.09951
378 TEAD4 TEAD4_(5981r) 0.04120 0.09945 0.11739
908 TEAD4 TEAD4_extended_(5981r) 0.04120 0.09945 0.11739
829 RIOK2 RIOK2_extended_(1192r) 0.04113 0.10001 0.11778
309 RIOK2 RIOK2_(1192r) 0.04113 0.10001 0.11778
460 ZNF579 ZNF579_(2476r) 0.04038 0.10643 0.12504
1000 ZNF579 ZNF579_extended_(2476r) 0.04038 0.10643 0.12504
809 RARA RARA_extended_(1596r) 0.04016 0.10834 0.12699
293 RARA RARA_(1596r) 0.04016 0.10834 0.12699
334 SOX13 SOX13_(5255r) 0.03747 0.13412 0.15561
968 ZNF12 ZNF12_extended_(16r) 0.03731 0.13576 0.15716
430 ZNF12 ZNF12_(16r) 0.03731 0.13576 0.15716
861 SOX13 SOX13_extended_(6135r) 0.03502 0.16142 0.18665
80 EHF EHF_(5266r) 0.03466 0.16579 0.19084
561 EHF EHF_extended_(5266r) 0.03466 0.16579 0.19084
243 NKX3-1 NKX3-1_(757r) 0.03418 0.17177 0.19683
750 NKX3-1 NKX3-1_extended_(757r) 0.03418 0.17177 0.19683
639 HMGB3 HMGB3_extended_(36r) 0.03202 0.20053 0.22877
654 INSM1 INSM1_extended_(653r) 0.03149 0.20800 0.23676
154 INSM1 INSM1_(653r) 0.03149 0.20800 0.23676
688 LEF1 LEF1_extended_(1246r) 0.03134 0.21025 0.23868
866 SOX3 SOX3_extended_(6078r) 0.03129 0.21094 0.23877
338 SOX3 SOX3_(6078r) 0.03129 0.21094 0.23877
956 ZC3H11A ZC3H11A_extended_(3904r) 0.02892 0.24766 0.27726
422 ZC3H11A ZC3H11A_(3904r) 0.02892 0.24766 0.27726
265 PBX2 PBX2_(44r) 0.02885 0.24877 0.27789
773 PBX2 PBX2_extended_(44r) 0.02885 0.24877 0.27789
24 BARX2 BARX2_(4563r) 0.02867 0.25169 0.28055
500 BARX2 BARX2_extended_(4563r) 0.02867 0.25169 0.28055
832 RUVBL1 RUVBL1_extended_(14r) 0.02798 0.26338 0.29229
311 RUVBL1 RUVBL1_(14r) 0.02798 0.26338 0.29229
909 TEF TEF_extended_(331r) 0.02735 0.27423 0.30368
904 TCF7 TCF7_extended_(137r) 0.02724 0.27626 0.30559
741 NFE2L3 NFE2L3_extended_(6701r) 0.02547 0.30865 0.34032
592 FOXK1 FOXK1_extended_(1529r) 0.02421 0.33308 0.36567
183 LEF1 LEF1_(1109r) 0.02386 0.34020 0.37228
837 SALL2 SALL2_extended_(236r) 0.02372 0.34296 0.37491
724 MYC MYC_extended_(11466r) 0.02333 0.35107 0.38255
217 MYC MYC_(11466r) 0.02333 0.35107 0.38255
914 TFE3 TFE3_extended_(86r) 0.02184 0.38272 0.41350
384 TFE3 TFE3_(86r) 0.02184 0.38272 0.41350
234 NFE2L3 NFE2L3_(5767r) 0.01918 0.44319 0.47681
848 SMAD5 SMAD5_extended_(43r) 0.01885 0.45126 0.48498
946 ZBTB17 ZBTB17_extended_(2947r) 0.01880 0.45237 0.48567
25 BATF3 BATF3_(96r) 0.01849 0.45983 0.49183
501 BATF3 BATF3_extended_(96r) 0.01849 0.45983 0.49183
400 VAX2 VAX2_(4758r) 0.01848 0.46004 0.49183
934 VAX2 VAX2_extended_(4758r) 0.01848 0.46004 0.49183
362 STAT5B STAT5B_(225r) 0.01843 0.46142 0.49228
891 STAT5B STAT5B_extended_(225r) 0.01843 0.46142 0.49228
597 FOXO1 FOXO1_extended_(8063r) 0.01763 0.48092 0.51255
111 FOXO1 FOXO1_(8043r) 0.01399 0.57605 0.61074
583 EXO5 EXO5_extended_(1104r) 0.01363 0.58592 0.61864
102 EXO5 EXO5_(1104r) 0.01363 0.58592 0.61864
776 PGAM2 PGAM2_extended_(957r) 0.01300 0.60337 0.63575
267 PGAM2 PGAM2_(957r) 0.01300 0.60337 0.63575
61 DDIT3 DDIT3_(59r) 0.01283 0.60808 0.63940
541 DDIT3 DDIT3_extended_(59r) 0.01283 0.60808 0.63940
978 ZNF267 ZNF267_extended_(261r) 0.01194 0.63331 0.66427
439 ZNF267 ZNF267_(261r) 0.01194 0.63331 0.66427
950 ZBTB2 ZBTB2_extended_(251r) 0.01190 0.63434 0.66427
414 ZBTB2 ZBTB2_(251r) 0.01190 0.63434 0.66427
429 ZMIZ1 ZMIZ1_(528r) 0.01141 0.64828 0.67747
967 ZMIZ1 ZMIZ1_extended_(528r) 0.01141 0.64828 0.67747
379 TEF TEF_(231r) 0.01119 0.65462 0.68340
129 HDAC2 HDAC2_(753r) 0.01079 0.66636 0.69424
620 HDAC2 HDAC2_extended_(753r) 0.01079 0.66636 0.69424
346 SP2 SP2_(7979r) 0.01000 0.68950 0.71761
315 SAP30 SAP30_(265r) 0.00761 0.76110 0.78731
839 SAP30 SAP30_extended_(265r) 0.00761 0.76110 0.78731
468 ZNF691 ZNF691_(391r) 0.00552 0.82527 0.84429
387 THAP11 THAP11_(24r) 0.00508 0.83924 0.85601
918 THAP11 THAP11_extended_(24r) 0.00508 0.83924 0.85601
591 FOXJ3 FOXJ3_extended_(4714r) 0.00504 0.84021 0.85614
1004 ZNF664 ZNF664_extended_(25r) 0.00468 0.85170 0.86612
465 ZNF664 ZNF664_(25r) 0.00468 0.85170 0.86612
926 TRPS1 TRPS1_extended_(397r) 0.00327 0.89598 0.90484
1017 ZSCAN21 ZSCAN21_extended_(213r) 0.00315 0.89972 0.90772
231 NFATC3 NFATC3_(961r) 0.00174 0.94469 0.94655
738 NFATC3 NFATC3_extended_(961r) 0.00174 0.94469 0.94655
261 PAX5 PAX5_(4427r) 0.00052 0.98330 0.98330
769 PAX5 PAX5_extended_(4427r) 0.00052 0.98330 0.98330
136 HEY1 HEY1_(86r) -0.00196 0.93749 0.94117
628 HEY1 HEY1_extended_(86r) -0.00196 0.93749 0.94117
441 ZNF281 ZNF281_(4149r) -0.00220 0.93008 0.93558
980 ZNF281 ZNF281_extended_(4149r) -0.00220 0.93008 0.93558
66 DLX6 DLX6_(4230r) -0.00257 0.91805 0.92530
556 EEF1AKMT3 EEF1AKMT3_extended_(1192r) -0.00357 0.88660 0.89625
75 EEF1AKMT3 EEF1AKMT3_(1192r) -0.00357 0.88660 0.89625
344 SOX9 SOX9_(9477r) -0.00366 0.88377 0.89516
634 HLF HLF_extended_(100r) -0.00412 0.86916 0.88124
307 RFXANK RFXANK_(4684r) -0.00448 0.85790 0.87069
827 RFXANK RFXANK_extended_(4684r) -0.00448 0.85790 0.87069
1008 ZNF691 ZNF691_extended_(3411r) -0.00524 0.83397 0.85233
18 ATF5 ATF5_(1849r) -0.00591 0.81336 0.83294
494 ATF5 ATF5_extended_(1849r) -0.00591 0.81336 0.83294
567 ELK4 ELK4_extended_(8636r) -0.00623 0.80331 0.82430
86 ELK4 ELK4_(8636r) -0.00623 0.80331 0.82430
573 ERF ERF_extended_(7355r) -0.00641 0.79775 0.82024
92 ERF ERF_(7355r) -0.00641 0.79775 0.82024
785 POLE4 POLE4_extended_(194r) -0.00687 0.78374 0.80747
872 SOX9 SOX9_extended_(9960r) -0.00736 0.76869 0.79276
1005 ZNF665 ZNF665_extended_(16r) -0.00740 0.76731 0.79214
466 ZNF665 ZNF665_(16r) -0.00740 0.76731 0.79214
23 BACH2 BACH2_(12072r) -0.00874 0.72674 0.75330
645 HSF2 HSF2_extended_(225r) -0.00879 0.72536 0.75263
499 BACH2 BACH2_extended_(12417r) -0.00950 0.70412 0.73134
723 MYBL2 MYBL2_extended_(990r) -0.00978 0.69583 0.72346
113 FOXO4 FOXO4_(1582r) -0.01368 0.58441 0.61833
599 FOXO4 FOXO4_extended_(1604r) -0.01372 0.58350 0.61801
204 MEOX2 MEOX2_(5105r) -0.01410 0.57307 0.60822
722 MYBL1 MYBL1_extended_(1490r) -0.01536 0.53929 0.57297
834 RXRB RXRB_extended_(1596r) -0.01639 0.51236 0.54492
313 RXRB RXRB_(1596r) -0.01639 0.51236 0.54492
489 ASCL1 ASCL1_extended_(6738r) -0.02001 0.42389 0.45653
576 ETS1 ETS1_extended_(11874r) -0.02135 0.39338 0.42412
711 MEOX2 MEOX2_extended_(6107r) -0.02169 0.38592 0.41652
114 FOXO6 FOXO6_(1582r) -0.02245 0.36951 0.40008
0 ACO1 ACO1_(376r) -0.02283 0.36153 0.39185
475 ACO1 ACO1_extended_(376r) -0.02283 0.36153 0.39185
756 NR1H2 NR1H2_extended_(6r) -0.02307 0.35646 0.38717
248 NR1H2 NR1H2_(6r) -0.02307 0.35646 0.38717
600 FOXO6 FOXO6_extended_(1604r) -0.02326 0.35251 0.38370
95 ETS1 ETS1_(11737r) -0.02370 0.34346 0.37505
894 SUPT20H SUPT20H_extended_(679r) -0.02388 0.33973 0.37217
365 SUPT20H SUPT20H_(679r) -0.02388 0.33973 0.37217
63 DLX1 DLX1_(6215r) -0.02481 0.32140 0.35323
546 DLX6 DLX6_extended_(4647r) -0.02482 0.32108 0.35323
13 ASCL1 ASCL1_(3842r) -0.02520 0.31372 0.34554
762 ODC1 ODC1_extended_(3063r) -0.02618 0.29533 0.32598
254 ODC1 ODC1_(3063r) -0.02618 0.29533 0.32598
109 FOXJ2 FOXJ2_(2449r) -0.02766 0.26879 0.29797
249 NR2C2 NR2C2_(1056r) -0.02832 0.25753 0.28642
757 NR2C2 NR2C2_extended_(1056r) -0.02832 0.25753 0.28642
721 MYB MYB_extended_(3721r) -0.02992 0.23159 0.25984
157 IRF3 IRF3_(4053r) -0.03011 0.22864 0.25681
759 NR3C1 NR3C1_extended_(3088r) -0.03025 0.22657 0.25477
251 NR3C1 NR3C1_(3088r) -0.03025 0.22657 0.25477
238 NFIX NFIX_(8846r) -0.03112 0.21347 0.24056
745 NFIX NFIX_extended_(8846r) -0.03112 0.21347 0.24056
718 MXD3 MXD3_extended_(86r) -0.03117 0.21270 0.24023
211 MXD3 MXD3_(86r) -0.03117 0.21270 0.24023
728 NANOG NANOG_extended_(9464r) -0.03133 0.21040 0.23868
657 IRF3 IRF3_extended_(4556r) -0.03140 0.20941 0.23809
543 DLX1 DLX1_extended_(6417r) -0.03300 0.18707 0.21365
526 CREB3 CREB3_extended_(195r) -0.03371 0.17777 0.20325
49 CREB3 CREB3_(195r) -0.03371 0.17777 0.20325
12 ARX ARX_(4213r) -0.03432 0.17008 0.19534
580 ETV4 ETV4_extended_(10218r) -0.03446 0.16831 0.19352
64 DLX2 DLX2_(3641r) -0.03467 0.16577 0.19084
544 DLX2 DLX2_extended_(3641r) -0.03467 0.16577 0.19084
160 IVD IVD_(67r) -0.03815 0.12714 0.14768
660 IVD IVD_extended_(67r) -0.03815 0.12714 0.14768
212 MXD4 MXD4_(86r) -0.03830 0.12570 0.14634
719 MXD4 MXD4_extended_(86r) -0.03830 0.12570 0.14634
357 STAT1 STAT1_(10312r) -0.03846 0.12407 0.14477
886 STAT1 STAT1_extended_(10312r) -0.03846 0.12407 0.14477
65 DLX5 DLX5_(5382r) -0.03962 0.11315 0.13233
545 DLX5 DLX5_extended_(5382r) -0.03962 0.11315 0.13233
99 ETV4 ETV4_(9117r) -0.04128 0.09881 0.11690
121 GTF2B GTF2B_(264r) -0.04167 0.09570 0.11335
611 GTF2B GTF2B_extended_(264r) -0.04167 0.09570 0.11335
103 EZH2 EZH2_(1103r) -0.04370 0.08054 0.09572
584 EZH2 EZH2_extended_(1103r) -0.04370 0.08054 0.09572
958 ZEB1 ZEB1_extended_(497r) -0.04397 0.07871 0.09378
424 ZEB1 ZEB1_(497r) -0.04397 0.07871 0.09378
339 SOX4 SOX4_(7897r) -0.04653 0.06278 0.07515
836 SALL1 SALL1_extended_(236r) -0.04658 0.06252 0.07492
320 SIRT6 SIRT6_(1627r) -0.04668 0.06194 0.07432
844 SIRT6 SIRT6_extended_(1627r) -0.04668 0.06194 0.07432
867 SOX4 SOX4_extended_(9442r) -0.04670 0.06181 0.07432
50 CREB3L1 CREB3L1_(86r) -0.04690 0.06073 0.07312
496 ATF6B ATF6B_extended_(178r) -0.04754 0.05729 0.06905
20 ATF6B ATF6B_(178r) -0.04754 0.05729 0.06905
70 E2F4 E2F4_(499r) -0.04804 0.05469 0.06623
303 RFX2 RFX2_(6591r) -0.04893 0.05035 0.06150
495 ATF6 ATF6_extended_(108r) -0.04900 0.05005 0.06135
19 ATF6 ATF6_(108r) -0.04900 0.05005 0.06135
821 RFX2 RFX2_extended_(7423r) -0.04926 0.04883 0.06017
605 GABPB1 GABPB1_extended_(7104r) -0.04989 0.04600 0.05734
118 GABPB1 GABPB1_(7104r) -0.04989 0.04600 0.05734
849 SMAD9 SMAD9_extended_(43r) -0.05085 0.04197 0.05258
638 HMGB2 HMGB2_extended_(36r) -0.05133 0.04009 0.05028
622 HDX HDX_extended_(1749r) -0.05180 0.03828 0.04808
131 HDX HDX_(1749r) -0.05180 0.03828 0.04808
93 ERG ERG_(9645r) -0.05307 0.03379 0.04264
412 ZBTB17 ZBTB17_(2476r) -0.05313 0.03358 0.04243
949 ZBTB26 ZBTB26_extended_(86r) -0.05569 0.02592 0.03299
416 ZBTB26 ZBTB26_(86r) -0.05569 0.02592 0.03299
888 STAT3 STAT3_extended_(19173r) -0.05596 0.02521 0.03225
359 STAT3 STAT3_(19173r) -0.05596 0.02521 0.03225
931 USF1 USF1_extended_(524r) -0.05656 0.02366 0.03035
397 USF1 USF1_(524r) -0.05656 0.02366 0.03035
221 NANOG NANOG_(8034r) -0.05740 0.02167 0.02786
488 ARX ARX_extended_(6017r) -0.05743 0.02160 0.02781
145 HNF4G HNF4G_(2401r) -0.06142 0.01401 0.01810
641 HNF4G HNF4G_extended_(2401r) -0.06142 0.01401 0.01810
225 NELFE NELFE_(654r) -0.06172 0.01355 0.01755
732 NELFE NELFE_extended_(654r) -0.06172 0.01355 0.01755
985 ZNF35 ZNF35_extended_(23r) -0.06316 0.01151 0.01503
264 PBX1 PBX1_(44r) -0.06439 0.00999 0.01306
409 YY2 YY2_(3219r) -0.06525 0.00903 0.01182
84 ELK1 ELK1_(10538r) -0.06585 0.00841 0.01103
565 ELK1 ELK1_extended_(10538r) -0.06585 0.00841 0.01103
943 YY2 YY2_extended_(3315r) -0.06607 0.00820 0.01077
574 ERG ERG_extended_(10296r) -0.06660 0.00770 0.01013
161 JDP2 JDP2_(11500r) -0.06714 0.00722 0.00951
778 PHF8 PHF8_extended_(829r) -0.06861 0.00604 0.00801
269 PHF8 PHF8_(829r) -0.06861 0.00604 0.00801
214 MYB MYB_(2733r) -0.07001 0.00509 0.00678
915 TFEB TFEB_extended_(86r) -0.07089 0.00456 0.00610
385 TFEB TFEB_(86r) -0.07089 0.00456 0.00610
551 E2F4 E2F4_extended_(2798r) -0.07095 0.00452 0.00606
467 ZNF671 ZNF671_(634r) -0.07165 0.00414 0.00556
1007 ZNF671 ZNF671_extended_(634r) -0.07165 0.00414 0.00556
661 JDP2 JDP2_extended_(11543r) -0.07297 0.00350 0.00472
755 NPDC1 NPDC1_extended_(92r) -0.07556 0.00249 0.00337
138 HEYL HEYL_(86r) -0.07785 0.00183 0.00248
630 HEYL HEYL_extended_(86r) -0.07785 0.00183 0.00248
811 RARG RARG_extended_(2912r) -0.07934 0.00149 0.00203
295 RARG RARG_(2912r) -0.07934 0.00149 0.00203
483 ARID3B ARID3B_extended_(581r) -0.08035 0.00130 0.00177
910 TFAP2C TFAP2C_extended_(1842r) -0.08186 0.00105 0.00143
380 TFAP2C TFAP2C_(1842r) -0.08186 0.00105 0.00143
481 ARGFX ARGFX_extended_(3626r) -0.08309 0.00088 0.00121
6 ARGFX ARGFX_(3626r) -0.08309 0.00088 0.00121
373 TCF3 TCF3_(4504r) -0.08474 0.00069 0.00095
870 SOX7 SOX7_extended_(6099r) -0.08595 0.00058 0.00080
281 POU3F3 POU3F3_(594r) -0.08597 0.00058 0.00080
989 ZNF415 ZNF415_extended_(20r) -0.08655 0.00053 0.00073
449 ZNF415 ZNF415_(20r) -0.08655 0.00053 0.00073
847 SMAD4 SMAD4_extended_(196r) -0.08656 0.00053 0.00073
323 SMAD4 SMAD4_(196r) -0.08656 0.00053 0.00073
159 IRF9 IRF9_(2399r) -0.08660 0.00052 0.00073
342 SOX7 SOX7_(4918r) -0.08667 0.00052 0.00072
1003 ZNF599 ZNF599_extended_(858r) -0.08688 0.00050 0.00070
463 ZNF599 ZNF599_(858r) -0.08688 0.00050 0.00070
902 TCF3 TCF3_extended_(5874r) -0.08804 0.00042 0.00059
33 BRF1 BRF1_(563r) -0.08883 0.00037 0.00052
509 BRF1 BRF1_extended_(563r) -0.08883 0.00037 0.00052
406 YBX1 YBX1_(312r) -0.08990 0.00032 0.00045
940 YBX1 YBX1_extended_(312r) -0.08990 0.00032 0.00045
299 RELA RELA_(9044r) -0.09060 0.00028 0.00040
885 SRF SRF_extended_(3748r) -0.09224 0.00022 0.00031
797 PPARA PPARA_extended_(36r) -0.09350 0.00018 0.00026
659 IRF9 IRF9_extended_(3095r) -0.09443 0.00016 0.00022
966 ZIC2 ZIC2_extended_(117r) -0.09516 0.00014 0.00020
68 E2F1 E2F1_(2918r) -0.09528 0.00014 0.00019
700 MBD1 MBD1_extended_(120r) -0.09542 0.00013 0.00019
878 SP8 SP8_extended_(299r) -0.09576 0.00013 0.00018
705 MEF2B MEF2B_extended_(4628r) -0.09597 0.00012 0.00017
772 PBX1 PBX1_extended_(168r) -0.09611 0.00012 0.00017
22 BACH1 BACH1_(15444r) -0.09683 0.00010 0.00015
199 MEF2B MEF2B_(3647r) -0.09736 0.00010 0.00014
432 ZNF143 ZNF143_(5277r) -0.09812 0.00008 0.00012
970 ZNF143 ZNF143_extended_(5287r) -0.09861 0.00008 0.00011
498 BACH1 BACH1_extended_(15532r) -0.09902 0.00007 0.00011
747 NFYB NFYB_extended_(2624r) -0.09985 0.00006 0.00009
240 NFYB NFYB_(2624r) -0.09985 0.00006 0.00009
427 ZFHX3 ZFHX3_(16r) -0.10068 0.00005 0.00008
961 ZFHX3 ZFHX3_extended_(16r) -0.10068 0.00005 0.00008
686 KLRG1 KLRG1_extended_(402r) -0.10145 0.00005 0.00007
349 SP8 SP8_(289r) -0.10163 0.00005 0.00007
180 KLF8 KLF8_(676r) -0.10204 0.00004 0.00006
549 E2F1 E2F1_extended_(3739r) -0.10326 0.00004 0.00005
272 PKNOX1 PKNOX1_(44r) -0.10373 0.00003 0.00005
816 RELA RELA_extended_(10218r) -0.10430 0.00003 0.00004
793 POU3F3 POU3F3_extended_(753r) -0.10515 0.00003 0.00004
860 SOX12 SOX12_extended_(7575r) -0.10529 0.00002 0.00004
333 SOX12 SOX12_(4438r) -0.10647 0.00002 0.00003
683 KLF7 KLF7_extended_(771r) -0.10742 0.00002 0.00002
897 TAF7 TAF7_extended_(2646r) -0.11144 0.00001 0.00001
368 TAF7 TAF7_(2646r) -0.11144 0.00001 0.00001
552 E2F5 E2F5_extended_(331r) -0.11151 0.00001 0.00001
71 E2F5 E2F5_(331r) -0.11151 0.00001 0.00001
428 ZFP82 ZFP82_(23r) -0.11173 0.00001 0.00001
964 ZFP82 ZFP82_extended_(23r) -0.11173 0.00001 0.00001
690 LHX6 LHX6_extended_(7672r) -0.11196 0.00001 0.00001
399 VAX1 VAX1_(4785r) -0.11321 0.00001 0.00001
933 VAX1 VAX1_extended_(4785r) -0.11321 0.00001 0.00001
588 FOSL2 FOSL2_extended_(15421r) -0.11381 0.00001 0.00001
916 TGIF1 TGIF1_extended_(36r) -0.11545 0.00000 0.00001
735 NF1 NF1_extended_(5487r) -0.11676 0.00000 0.00000
228 NF1 NF1_(5487r) -0.11676 0.00000 0.00000
107 FOSL2 FOSL2_(14343r) -0.11680 0.00000 0.00000
237 NFIC NFIC_(19254r) -0.11690 0.00000 0.00000
744 NFIC NFIC_extended_(19254r) -0.11690 0.00000 0.00000
142 HMGA1 HMGA1_(1300r) -0.11735 0.00000 0.00000
635 HMGA1 HMGA1_extended_(1300r) -0.11735 0.00000 0.00000
358 STAT2 STAT2_(2698r) -0.11965 0.00000 0.00000
887 STAT2 STAT2_extended_(2698r) -0.11965 0.00000 0.00000
835 RXRG RXRG_extended_(1596r) -0.11989 0.00000 0.00000
314 RXRG RXRG_(1596r) -0.11989 0.00000 0.00000
604 GABPA GABPA_extended_(9943r) -0.12108 0.00000 0.00000
117 GABPA GABPA_(9677r) -0.12263 0.00000 0.00000
527 CREB3L1 CREB3L1_extended_(178r) -0.12335 0.00000 0.00000
356 SRF SRF_(2591r) -0.12348 0.00000 0.00000
421 ZBTB7B ZBTB7B_(6347r) -0.12418 0.00000 0.00000
955 ZBTB7B ZBTB7B_extended_(6347r) -0.12418 0.00000 0.00000
393 TRIP10 TRIP10_(119r) -0.12435 0.00000 0.00000
925 TRIP10 TRIP10_extended_(119r) -0.12435 0.00000 0.00000
58 CYB5R1 CYB5R1_(15r) -0.12648 0.00000 0.00000
538 CYB5R1 CYB5R1_extended_(15r) -0.12648 0.00000 0.00000
1020 ZXDC ZXDC_extended_(124r) -0.12774 0.00000 0.00000
181 KLF9 KLF9_(767r) -0.12880 0.00000 0.00000
685 KLF9 KLF9_extended_(767r) -0.12880 0.00000 0.00000
621 HDAC6 HDAC6_extended_(1000r) -0.12940 0.00000 0.00000
130 HDAC6 HDAC6_(1000r) -0.12940 0.00000 0.00000
647 HSF5 HSF5_extended_(126r) -0.13016 0.00000 0.00000
148 HSF5 HSF5_(126r) -0.13016 0.00000 0.00000
520 CHD2 CHD2_extended_(850r) -0.13045 0.00000 0.00000
44 CHD2 CHD2_(850r) -0.13045 0.00000 0.00000
895 SUZ12 SUZ12_extended_(654r) -0.13070 0.00000 0.00000
366 SUZ12 SUZ12_(654r) -0.13070 0.00000 0.00000
1002 ZNF597 ZNF597_extended_(43r) -0.13106 0.00000 0.00000
462 ZNF597 ZNF597_(43r) -0.13106 0.00000 0.00000
537 CXXC1 CXXC1_extended_(5r) -0.13107 0.00000 0.00000
57 CXXC1 CXXC1_(5r) -0.13107 0.00000 0.00000
733 NEUROD1 NEUROD1_extended_(4280r) -0.13109 0.00000 0.00000
226 NEUROD1 NEUROD1_(4280r) -0.13109 0.00000 0.00000
781 PKNOX1 PKNOX1_extended_(80r) -0.13153 0.00000 0.00000
578 ETV1 ETV1_extended_(3619r) -0.13407 0.00000 0.00000
554 E4F1 E4F1_extended_(48r) -0.13497 0.00000 0.00000
73 E4F1 E4F1_(48r) -0.13497 0.00000 0.00000
185 LHX6 LHX6_(4677r) -0.13572 0.00000 0.00000
146 HOMEZ HOMEZ_(936r) -0.13598 0.00000 0.00000
642 HOMEZ HOMEZ_extended_(936r) -0.13598 0.00000 0.00000
593 FOXK2 FOXK2_extended_(1529r) -0.13626 0.00000 0.00000
951 ZBTB33 ZBTB33_extended_(839r) -0.13695 0.00000 0.00000
417 ZBTB33 ZBTB33_(839r) -0.13695 0.00000 0.00000
675 KLF13 KLF13_extended_(2574r) -0.13724 0.00000 0.00000
171 KLF13 KLF13_(2574r) -0.13724 0.00000 0.00000
771 PAX7 PAX7_extended_(2857r) -0.13906 0.00000 0.00000
263 PAX7 PAX7_(2712r) -0.14049 0.00000 0.00000
97 ETV1 ETV1_(2963r) -0.14286 0.00000 0.00000
259 PARP1 PARP1_(225r) -0.14315 0.00000 0.00000
767 PARP1 PARP1_extended_(225r) -0.14315 0.00000 0.00000
394 TWIST1 TWIST1_(877r) -0.14315 0.00000 0.00000
927 TWIST1 TWIST1_extended_(877r) -0.14315 0.00000 0.00000
996 ZNF519 ZNF519_extended_(24r) -0.14389 0.00000 0.00000
456 ZNF519 ZNF519_(24r) -0.14389 0.00000 0.00000
268 PGR PGR_(78r) -0.14639 0.00000 0.00000
777 PGR PGR_extended_(78r) -0.14639 0.00000 0.00000
196 MDM2 MDM2_(140r) -0.14660 0.00000 0.00000
702 MDM2 MDM2_extended_(140r) -0.14660 0.00000 0.00000
134 HES6 HES6_(86r) -0.14723 0.00000 0.00000
626 HES6 HES6_extended_(86r) -0.14723 0.00000 0.00000
301 RFC2 RFC2_(693r) -0.15120 0.00000 0.00000
819 RFC2 RFC2_extended_(693r) -0.15120 0.00000 0.00000
273 PLAGL1 PLAGL1_(1890r) -0.15284 0.00000 0.00000
783 PLAGL1 PLAGL1_extended_(1890r) -0.15284 0.00000 0.00000
332 SOX11 SOX11_(4668r) -0.15745 0.00000 0.00000
879 SP9 SP9_extended_(299r) -0.15757 0.00000 0.00000
805 PSMD12 PSMD12_extended_(5776r) -0.15937 0.00000 0.00000
289 PSMD12 PSMD12_(5776r) -0.15937 0.00000 0.00000
531 CREBL2 CREBL2_extended_(92r) -0.16000 0.00000 0.00000
859 SOX11 SOX11_extended_(7513r) -0.16165 0.00000 0.00000
54 CTBP2 CTBP2_(2412r) -0.16191 0.00000 0.00000
534 CTBP2 CTBP2_extended_(2412r) -0.16191 0.00000 0.00000
670 KDM5A KDM5A_extended_(139r) -0.16372 0.00000 0.00000
166 KDM5A KDM5A_(139r) -0.16372 0.00000 0.00000
807 R3HDM2 R3HDM2_extended_(10r) -0.16423 0.00000 0.00000
291 R3HDM2 R3HDM2_(10r) -0.16423 0.00000 0.00000
360 STAT4 STAT4_(225r) -0.16553 0.00000 0.00000
889 STAT4 STAT4_extended_(225r) -0.16553 0.00000 0.00000
681 KLF5 KLF5_extended_(2614r) -0.16586 0.00000 0.00000
177 KLF5 KLF5_(2614r) -0.16586 0.00000 0.00000
941 YOD1 YOD1_extended_(6112r) -0.16680 0.00000 0.00000
632 HINFP HINFP_extended_(3r) -0.16733 0.00000 0.00000
140 HINFP HINFP_(3r) -0.16733 0.00000 0.00000
407 YOD1 YOD1_(4874r) -0.16815 0.00000 0.00000
1016 ZRSR2 ZRSR2_extended_(59r) -0.16855 0.00000 0.00000
474 ZRSR2 ZRSR2_(59r) -0.16855 0.00000 0.00000
795 POU6F1 POU6F1_extended_(6482r) -0.17243 0.00000 0.00000
350 SP9 SP9_(289r) -0.17280 0.00000 0.00000
953 ZBTB49 ZBTB49_extended_(2712r) -0.17283 0.00000 0.00000
419 ZBTB49 ZBTB49_(2712r) -0.17283 0.00000 0.00000
615 GTF3C2 GTF3C2_extended_(528r) -0.17472 0.00000 0.00000
124 GTF3C2 GTF3C2_(528r) -0.17472 0.00000 0.00000
283 POU6F1 POU6F1_(5518r) -0.17661 0.00000 0.00000
684 KLF8 KLF8_extended_(866r) -0.17800 0.00000 0.00000
386 THAP1 THAP1_(28r) -0.18028 0.00000 0.00000
919 THAP1 THAP1_extended_(28r) -0.18028 0.00000 0.00000
752 NMI NMI_extended_(552r) -0.18222 0.00000 0.00000
245 NMI NMI_(552r) -0.18222 0.00000 0.00000
975 ZNF236 ZNF236_extended_(1306r) -0.18309 0.00000 0.00000
436 ZNF236 ZNF236_(1306r) -0.18309 0.00000 0.00000
863 SOX1 SOX1_extended_(5130r) -0.18463 0.00000 0.00000
179 KLF7 KLF7_(581r) -0.18532 0.00000 0.00000
590 FOXJ2 FOXJ2_extended_(3957r) -0.18599 0.00000 0.00000
648 ID1 ID1_extended_(86r) -0.18726 0.00000 0.00000
149 ID1 ID1_(86r) -0.18726 0.00000 0.00000
631 HIF1A HIF1A_extended_(348r) -0.18878 0.00000 0.00000
139 HIF1A HIF1A_(348r) -0.18878 0.00000 0.00000
665 KAT2A KAT2A_extended_(1405r) -0.19147 0.00000 0.00000
165 KAT2A KAT2A_(1405r) -0.19147 0.00000 0.00000
59 DBP DBP_(1004r) -0.19249 0.00000 0.00000
539 DBP DBP_extended_(1104r) -0.19262 0.00000 0.00000
610 GRHL1 GRHL1_extended_(435r) -0.19304 0.00000 0.00000
990 ZNF440 ZNF440_extended_(24r) -0.19313 0.00000 0.00000
450 ZNF440 ZNF440_(24r) -0.19313 0.00000 0.00000
321 SIX5 SIX5_(4470r) -0.19468 0.00000 0.00000
845 SIX5 SIX5_extended_(4470r) -0.19468 0.00000 0.00000
504 BCLAF1 BCLAF1_extended_(3207r) -0.19615 0.00000 0.00000
28 BCLAF1 BCLAF1_(3207r) -0.19615 0.00000 0.00000
413 ZBTB18 ZBTB18_(4712r) -0.19775 0.00000 0.00000
947 ZBTB18 ZBTB18_extended_(4712r) -0.19775 0.00000 0.00000
31 BHLHE41 BHLHE41_(86r) -0.19778 0.00000 0.00000
507 BHLHE41 BHLHE41_extended_(86r) -0.19778 0.00000 0.00000
553 E2F6 E2F6_extended_(495r) -0.19807 0.00000 0.00000
72 E2F6 E2F6_(495r) -0.19807 0.00000 0.00000
147 HOXD1 HOXD1_(4551r) -0.19978 0.00000 0.00000
607 GMEB1 GMEB1_extended_(12r) -0.19984 0.00000 0.00000
388 THRA THRA_(1596r) -0.20188 0.00000 0.00000
920 THRA THRA_extended_(1596r) -0.20188 0.00000 0.00000
905 TCF7L1 TCF7L1_extended_(1986r) -0.20200 0.00000 0.00000
643 HOXD1 HOXD1_extended_(5962r) -0.20205 0.00000 0.00000
656 IRF2 IRF2_extended_(5073r) -0.20276 0.00000 0.00000
513 CDC5L CDC5L_extended_(40r) -0.20398 0.00000 0.00000
37 CDC5L CDC5L_(40r) -0.20398 0.00000 0.00000
784 POLE3 POLE3_extended_(124r) -0.20405 0.00000 0.00000
5 AR AR_(1501r) -0.20440 0.00000 0.00000
480 AR AR_extended_(1501r) -0.20440 0.00000 0.00000
156 IRF2 IRF2_(4420r) -0.20446 0.00000 0.00000
525 CREB1 CREB1_extended_(677r) -0.20555 0.00000 0.00000
48 CREB1 CREB1_(677r) -0.20555 0.00000 0.00000
355 SREBF2 SREBF2_(3010r) -0.20653 0.00000 0.00000
884 SREBF2 SREBF2_extended_(3010r) -0.20653 0.00000 0.00000
375 TCF7L1 TCF7L1_(1874r) -0.20967 0.00000 0.00000
912 TFDP1 TFDP1_extended_(331r) -0.21404 0.00000 0.00000
382 TFDP1 TFDP1_(331r) -0.21404 0.00000 0.00000
505 BDP1 BDP1_extended_(585r) -0.21497 0.00000 0.00000
29 BDP1 BDP1_(585r) -0.21497 0.00000 0.00000
87 EMX1 EMX1_(4912r) -0.21647 0.00000 0.00000
917 TGIF2 TGIF2_extended_(36r) -0.21667 0.00000 0.00000
330 SOX1 SOX1_(3641r) -0.21797 0.00000 0.00000
987 ZNF384 ZNF384_extended_(20r) -0.21866 0.00000 0.00000
448 ZNF384 ZNF384_(20r) -0.21866 0.00000 0.00000
748 NFYC NFYC_extended_(317r) -0.21953 0.00000 0.00000
257 ONECUT2 ONECUT2_(15r) -0.22008 0.00000 0.00000
765 ONECUT2 ONECUT2_extended_(15r) -0.22008 0.00000 0.00000
737 NFATC2 NFATC2_extended_(129r) -0.22182 0.00000 0.00000
230 NFATC2 NFATC2_(129r) -0.22182 0.00000 0.00000
472 ZNF76 ZNF76_(22r) -0.22201 0.00000 0.00000
1013 ZNF76 ZNF76_extended_(22r) -0.22201 0.00000 0.00000
241 NFYC NFYC_(220r) -0.22216 0.00000 0.00000
215 MYBL1 MYBL1_(77r) -0.22234 0.00000 0.00000
125 GTPBP6 GTPBP6_(104r) -0.22238 0.00000 0.00000
616 GTPBP6 GTPBP6_extended_(104r) -0.22238 0.00000 0.00000
395 UBTF UBTF_(654r) -0.22251 0.00000 0.00000
929 UBTF UBTF_extended_(654r) -0.22251 0.00000 0.00000
608 GMEB2 GMEB2_extended_(4r) -0.22305 0.00000 0.00000
119 GMEB2 GMEB2_(4r) -0.22305 0.00000 0.00000
67 DNMT3A DNMT3A_(25r) -0.22457 0.00000 0.00000
547 DNMT3A DNMT3A_extended_(25r) -0.22457 0.00000 0.00000
942 YY1 YY1_extended_(4284r) -0.22861 0.00000 0.00000
133 HES5 HES5_(206r) -0.22923 0.00000 0.00000
625 HES5 HES5_extended_(206r) -0.22923 0.00000 0.00000
900 TBP TBP_extended_(4475r) -0.22953 0.00000 0.00000
371 TBP TBP_(4475r) -0.22953 0.00000 0.00000
568 EMX1 EMX1_extended_(6815r) -0.23113 0.00000 0.00000
112 FOXO3 FOXO3_(1582r) -0.23117 0.00000 0.00000
598 FOXO3 FOXO3_extended_(1604r) -0.23139 0.00000 0.00000
786 POLR2A POLR2A_extended_(10541r) -0.23372 0.00000 0.00000
274 POLR2A POLR2A_(10541r) -0.23372 0.00000 0.00000
408 YY1 YY1_(4207r) -0.23451 0.00000 0.00000
518 CEBPZ CEBPZ_extended_(265r) -0.23678 0.00000 0.00000
42 CEBPZ CEBPZ_(265r) -0.23678 0.00000 0.00000
192 MAGEF1 MAGEF1_(523r) -0.23794 0.00000 0.00000
697 MAGEF1 MAGEF1_extended_(523r) -0.23794 0.00000 0.00000
297 RBBP5 RBBP5_(1087r) -0.23949 0.00000 0.00000
813 RBBP5 RBBP5_extended_(1087r) -0.23949 0.00000 0.00000
189 MAFF MAFF_(7463r) -0.24704 0.00000 0.00000
694 MAFF MAFF_extended_(7866r) -0.24781 0.00000 0.00000
367 TAF1 TAF1_(3172r) -0.25330 0.00000 0.00000
896 TAF1 TAF1_extended_(3172r) -0.25330 0.00000 0.00000
137 HEY2 HEY2_(86r) -0.25355 0.00000 0.00000
629 HEY2 HEY2_extended_(86r) -0.25355 0.00000 0.00000
763 OLIG1 OLIG1_extended_(2188r) -0.25741 0.00000 0.00000
255 OLIG1 OLIG1_(2188r) -0.25741 0.00000 0.00000
276 POU2AF1 POU2AF1_(599r) -0.26187 0.00000 0.00000
788 POU2AF1 POU2AF1_extended_(599r) -0.26187 0.00000 0.00000
612 GTF2F1 GTF2F1_extended_(932r) -0.26495 0.00000 0.00000
122 GTF2F1 GTF2F1_(932r) -0.26495 0.00000 0.00000
651 IKZF2 IKZF2_extended_(1814r) -0.26514 0.00000 0.00000
151 IKZF2 IKZF2_(1814r) -0.26514 0.00000 0.00000
637 HMGB1 HMGB1_extended_(36r) -0.26710 0.00000 0.00000
840 SATB1 SATB1_extended_(301r) -0.26854 0.00000 0.00000
316 SATB1 SATB1_(301r) -0.26854 0.00000 0.00000
798 PPARD PPARD_extended_(36r) -0.27248 0.00000 0.00000
1006 ZNF66 ZNF66_extended_(66r) -0.27415 0.00000 0.00000
464 ZNF66 ZNF66_(66r) -0.27415 0.00000 0.00000
862 SOX15 SOX15_extended_(6945r) -0.27637 0.00000 0.00000
335 SOX15 SOX15_(6878r) -0.27663 0.00000 0.00000
108 FOXG1 FOXG1_(430r) -0.27696 0.00000 0.00000
94 ESRRA ESRRA_(528r) -0.28076 0.00000 0.00000
575 ESRRA ESRRA_extended_(528r) -0.28076 0.00000 0.00000
132 HES1 HES1_(302r) -0.28317 0.00000 0.00000
563 ELF2 ELF2_extended_(10812r) -0.28350 0.00000 0.00000
587 FOSB FOSB_extended_(13637r) -0.28460 0.00000 0.00000
106 FOSB FOSB_(13637r) -0.28460 0.00000 0.00000
82 ELF2 ELF2_(9568r) -0.28569 0.00000 0.00000
623 HES1 HES1_extended_(2956r) -0.29246 0.00000 0.00000
913 TFDP2 TFDP2_extended_(331r) -0.30290 0.00000 0.00000
383 TFDP2 TFDP2_(331r) -0.30290 0.00000 0.00000
779 PICK1 PICK1_extended_(4544r) -0.30300 0.00000 0.00000
270 PICK1 PICK1_(4544r) -0.30300 0.00000 0.00000
318 SF1 SF1_(548r) -0.30692 0.00000 0.00000
341 SOX6 SOX6_(6716r) -0.30779 0.00000 0.00000
869 SOX6 SOX6_extended_(7658r) -0.30812 0.00000 0.00000
846 SMAD1 SMAD1_extended_(4859r) -0.30839 0.00000 0.00000
322 SMAD1 SMAD1_(4816r) -0.30852 0.00000 0.00000
938 XBP1 XBP1_extended_(92r) -0.30941 0.00000 0.00000
404 XBP1 XBP1_(92r) -0.30941 0.00000 0.00000
116 FOXP2 FOXP2_(2027r) -0.31269 0.00000 0.00000
602 FOXP2 FOXP2_extended_(2149r) -0.31440 0.00000 0.00000
246 NNT NNT_(47r) -0.31531 0.00000 0.00000
753 NNT NNT_extended_(47r) -0.31531 0.00000 0.00000
775 PBX4 PBX4_extended_(268r) -0.31767 0.00000 0.00000
663 JUNB JUNB_extended_(16806r) -0.31945 0.00000 0.00000
123 GTF2I GTF2I_(396r) -0.32089 0.00000 0.00000
613 GTF2I GTF2I_extended_(396r) -0.32089 0.00000 0.00000
163 JUNB JUNB_(15981r) -0.32092 0.00000 0.00000
364 STAU2 STAU2_(129r) -0.32252 0.00000 0.00000
893 STAU2 STAU2_extended_(129r) -0.32252 0.00000 0.00000
423 ZDHHC15 ZDHHC15_(85r) -0.32462 0.00000 0.00000
957 ZDHHC15 ZDHHC15_extended_(85r) -0.32462 0.00000 0.00000
790 POU2F2 POU2F2_extended_(4190r) -0.32517 0.00000 0.00000
823 RFX4 RFX4_extended_(6569r) -0.32569 0.00000 0.00000
162 JUN JUN_(17924r) -0.32614 0.00000 0.00000
305 RFX4 RFX4_(5602r) -0.32803 0.00000 0.00000
662 JUN JUN_extended_(19322r) -0.32970 0.00000 0.00000
965 ZIC1 ZIC1_extended_(117r) -0.33152 0.00000 0.00000
167 KDM5B KDM5B_(126r) -0.33200 0.00000 0.00000
671 KDM5B KDM5B_extended_(126r) -0.33200 0.00000 0.00000
954 ZBTB7A ZBTB7A_extended_(775r) -0.34002 0.00000 0.00000
420 ZBTB7A ZBTB7A_(775r) -0.34002 0.00000 0.00000
730 NCALD NCALD_extended_(101r) -0.34566 0.00000 0.00000
223 NCALD NCALD_(101r) -0.34566 0.00000 0.00000
624 HES4 HES4_extended_(2654r) -0.35091 0.00000 0.00000
736 NFAT5 NFAT5_extended_(1830r) -0.35496 0.00000 0.00000
229 NFAT5 NFAT5_(1830r) -0.35496 0.00000 0.00000
91 EPAS1 EPAS1_(348r) -0.35528 0.00000 0.00000
572 EPAS1 EPAS1_extended_(348r) -0.35528 0.00000 0.00000
197 MECP2 MECP2_(685r) -0.35559 0.00000 0.00000
703 MECP2 MECP2_extended_(685r) -0.35559 0.00000 0.00000
883 SREBF1 SREBF1_extended_(7120r) -0.35583 0.00000 0.00000
354 SREBF1 SREBF1_(7120r) -0.35583 0.00000 0.00000
244 NKX6-2 NKX6-2_(6335r) -0.35669 0.00000 0.00000
751 NKX6-2 NKX6-2_extended_(7201r) -0.35833 0.00000 0.00000
172 KLF15 KLF15_(3441r) -0.35998 0.00000 0.00000
676 KLF15 KLF15_extended_(3468r) -0.36034 0.00000 0.00000
712 MITF MITF_extended_(86r) -0.36169 0.00000 0.00000
205 MITF MITF_(86r) -0.36169 0.00000 0.00000
492 ATF3 ATF3_extended_(14221r) -0.37251 0.00000 0.00000
818 REST REST_extended_(5605r) -0.37460 0.00000 0.00000
533 CREM CREM_extended_(171r) -0.37861 0.00000 0.00000
53 CREM CREM_(171r) -0.37861 0.00000 0.00000
16 ATF3 ATF3_(12847r) -0.37888 0.00000 0.00000
596 FOXN3 FOXN3_extended_(836r) -0.38157 0.00000 0.00000
780 PKM PKM_extended_(685r) -0.39253 0.00000 0.00000
271 PKM PKM_(685r) -0.39253 0.00000 0.00000
300 REST REST_(2933r) -0.39328 0.00000 0.00000
842 SF1 SF1_extended_(763r) -0.39691 0.00000 0.00000
800 PRDM11 PRDM11_extended_(826r) -0.40485 0.00000 0.00000
935 VEZF1 VEZF1_extended_(2975r) -0.41023 0.00000 0.00000
401 VEZF1 VEZF1_(2975r) -0.41023 0.00000 0.00000
998 ZNF540 ZNF540_extended_(214r) -0.42891 0.00000 0.00000
458 ZNF540 ZNF540_(214r) -0.42891 0.00000 0.00000
286 PRNP PRNP_(834r) -0.43229 0.00000 0.00000
802 PRNP PRNP_extended_(834r) -0.43229 0.00000 0.00000
586 FOS FOS_extended_(17541r) -0.44315 0.00000 0.00000
105 FOS FOS_(17541r) -0.44315 0.00000 0.00000
799 PPARG PPARG_extended_(36r) -0.44770 0.00000 0.00000
476 ADARB1 ADARB1_extended_(493r) -0.47795 0.00000 0.00000
1 ADARB1 ADARB1_(493r) -0.47795 0.00000 0.00000
170 KLF12 KLF12_(2792r) -0.47878 0.00000 0.00000
782 PKNOX2 PKNOX2_extended_(36r) -0.48793 0.00000 0.00000
296 RB1 RB1_(817r) -0.49248 0.00000 0.00000
812 RB1 RB1_extended_(817r) -0.49248 0.00000 0.00000
674 KLF12 KLF12_extended_(2843r) -0.49319 0.00000 0.00000
233 NFE2L2 NFE2L2_(8999r) -0.49746 0.00000 0.00000
589 FOXG1 FOXG1_extended_(1956r) -0.49862 0.00000 0.00000
740 NFE2L2 NFE2L2_extended_(9438r) -0.50545 0.00000 0.00000
550 E2F3 E2F3_extended_(486r) -0.53414 0.00000 0.00000
69 E2F3 E2F3_(397r) -0.53585 0.00000 0.00000
868 SOX5 SOX5_extended_(7972r) -0.55393 0.00000 0.00000
340 SOX5 SOX5_(6811r) -0.56024 0.00000 0.00000
830 RREB1 RREB1_extended_(446r) -0.57808 0.00000 0.00000

We can filter cistromes based on their correlation values (or p-value). We recommend however to use these values as a method to prioritize eGRNs, without applying a strict threshold.

[14]:
print('Number of unfiltered cistromes:')
print(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered'].shape[0])
print('Number of cistromes with padj < 0.0001:')
print(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered'][scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered']['Adjusted_p-value'] < 0.0001].shape[0])
Number of unfiltered cistromes:
1021
Number of cistromes with padj < 0.0001:
703
[15]:
# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  pickle.dump(scplus_obj, f)

D. Visualization

I. TF expression versus AUC values

We can visualize TF versus cistrome expression:

[16]:
%matplotlib inline
sns.set_style("white")
colors = ["#E9842C","#F8766D", "#BC9D00", "#00C0B4", "#9CA700", "#6FB000", "#00B813", "#00BD61", "#00C08E", "#00BDD4",
           "#00A7FF", "#7F96FF", "#E26EF7", "#FF62BF", "#D69100", "#BC81FF"]
categories = sorted(set(scplus_obj.metadata_cell['ACC_Seurat_cell_type']))
color_dict = dict(zip(categories, colors[0:len(categories)]))
prune_plot(scplus_obj,
           'IKZF1',
           pseudobulk_variable = 'ACC_Seurat_cell_type',
           show_dot_plot = True,
           show_line_plot = False,
           color_dict = color_dict,
           use_pseudobulk = True,
           signature_key = 'Unfiltered',
           seed=555)
_images/single_sample_tutorial_40_0.png

II. Dot plot

We can generate a dotplot as well based on the AUC values or the motif enrichment results.

[17]:
# Load functions
from scenicplus.plotting.dotplot import *
# Load data
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

In this example we will select a subset of the cistromes based on the TF-AUC correlation.

[18]:
# Direct cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered']
selected_cistromes_direct = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.4)]
# Extended cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_unfiltered']
selected_cistromes_extended = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.4)]
# Only use extended if direct not available
selected_cistromes_extended = selected_cistromes_extended[~selected_cistromes_extended['TF'].isin(selected_cistromes_direct['TF'])]
# Combine
selected_cistromes = pd.concat([selected_cistromes_direct, selected_cistromes_extended])
len(selected_cistromes)
[18]:
68

We first generate the dotplot dataframe, that contains information about the cistrome enrichment (AUC in this case) and TF expression per group.

[20]:
dotplot_df = generate_dotplot_df_AUC(scplus_obj,
                       'Cistromes_AUC',
                       'Unfiltered',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_cistromes = selected_cistromes['Cistrome'].tolist(),
                       use_pseudobulk = False,
                       normalize_expression = False,
                       standardize_expression = True,
                       standardize_auc = True)
[23]:
import os
import plotly.io as pio
# Kaleido options to save plot
pio.kaleido.scope.chromium_args = tuple([arg for arg in pio.kaleido.scope.chromium_args if arg != "--disable-dev-shm-usage"])
dotplot(dotplot_df,
         ax = None,
         order_cistromes_by_max = 'TF_expression',
         cluster = 'group', #can be group, TF or both
         color_var = 'Cistromes_AUC',
         size_var = 'TF_expression',
         order_group = None,
         order_cistromes = None,
         n_clust = 10,
         min_point_size = 1,
         max_point_size = 15,
         cmap = 'viridis',
         vmin = 0,
         vmax = 1,
         x_tick_rotation = 45,
         x_tick_ha = 'right',
         fontsize  = 9,
         z_score_expr = False,
         z_score_enr = False,
         grid_color = 'grey',
         grid_lw = 0.5,
         highlight = None,
         highlight_lw = 1,
         use_plotly = True,
        save='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/dotplot_AUC.pdf')

We can also make these dotplot based on the cisTarget and DEM motif enrichment results.

[25]:
# CTX df
dotplot_df = generate_dotplot_df_motif_enrichment(scplus_obj,
                       'CTX_DARs_All',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_TFs = list(set(selected_cistromes.TF)),
                       use_pseudobulk = False,
                       normalize_expression = True,
                       standardize_expression = False)
# Plot
dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'TF',
                     order_cistromes_by_max = 'TF_expression',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'NES',
                     size_var = 'TF_expression',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     use_plotly = True)
[26]:
# DEM df
dotplot_df = generate_dotplot_df_motif_enrichment(scplus_obj,
                       'DEM_DARs_All',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_TFs = list(set(selected_cistromes.TF)),
                       use_pseudobulk = False,
                       normalize_expression = True,
                       standardize_expression = False)
# Plot
dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'TF',
                     order_cistromes_by_max = 'TF_expression',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'Log2FC',
                     size_var = 'TF_expression',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     use_plotly = True)

In addition, it is also possible to do it for motif enrichment tables that are not linked to a metadata variable, such as topics. In this cases, we will have to provide a dictionary with the barcodes to use to calculate the TF expresssion in each group.

[27]:
# Binarized cell-topic distributions from pycisTopic
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/topic_binarization/binarized_cell_topic.pkl', 'rb')
binarized_cell_topic = pickle.load(infile)
infile.close()
# Create barcode dictionary
barcode_groups = {x: binarized_cell_topic[x].index.tolist() for x in binarized_cell_topic.keys()}
[28]:
# Generate dotplot
dotplot_df = generate_dotplot_df_motif_enrichment(scplus_obj,
                       'CTX_Topics_All',
                       group_variable = None,
                       barcode_groups = barcode_groups,
                       subset = None,
                       subset_TFs = list(set(selected_cistromes.TF)),
                       use_pseudobulk = False,
                       normalize_expression = True,
                       standardize_expression = True)

dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'TF',
                     order_cistromes_by_max = 'TF_expression',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'NES',
                     size_var = 'TF_expression',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     use_plotly = True)

4. Infer enhancer to gene relationships

To infer enhancer-to-gene relationships, we exploit correlation between region accessibility and gene expression. In addition, to assess no non-linear relationships, we also use Gradient Boosting Machines. More details on these steps can be found in the tutorial r2g_advanced.ipynb.

A. Get search space

We first need to define the search space around the gene. Here we will use 150kb upstream/downtream the gene, but TAD boundaries can be also used. WARNING: Make sure that the specified biomart_host matches your genome assembly.

[29]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[30]:
from scenicplus.enhancer_to_gene import get_search_space, calculate_regions_to_genes_relationships, GBM_KWARGS
get_search_space(scplus_obj,
                 biomart_host = 'http://www.ensembl.org',
                 species = 'hsapiens',
                 assembly = 'hg38',
                 upstream = [1000, 150000],
                 downstream = [1000, 150000])
2022-01-04 17:50:07,795 R2G          INFO     Downloading gene annotation from biomart dataset: hsapiens_gene_ensembl
/opt/venv/lib/python3.8/site-packages/scenicplus/enhancer_to_gene.py:197: DtypeWarning:

Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.

2022-01-04 17:50:40,505 R2G          INFO     Downloading chromosome sizes from: http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
2022-01-04 17:50:42,099 R2G          INFO     Extending promoter annotation to 10 bp upstream and 10 downstream
2022-01-04 17:50:44,632 R2G          INFO     Extending search space to:
                                                        150000 bp downstream of the end of the gene.
                                                        150000 bp upstream of the start of the gene.
2022-01-04 17:51:07,232 R2G          INFO     Intersecting with regions.
join: Strand data from other will be added as strand data to self.
If this is undesired use the flag apply_strand_suffix=False.
To turn off the warning set apply_strand_suffix to True or False.
2022-01-04 17:51:08,592 R2G          INFO     Calculating distances from region to gene
2022-01-04 17:55:43,084 R2G          INFO     Imploding multiple entries per region and gene
2022-01-04 17:58:01,862 R2G          INFO     Done!

B. Enhancer-to-gene models

Enhancer-to-gene models can be done using correlation, random forest (RF) or Gradient Boosting Machines (GBM). GBMs are a much faster alternative to RFs.

[31]:
calculate_regions_to_genes_relationships(scplus_obj,
                    ray_n_cpu = 20,
                    _temp_dir = '/scratch/leuven/313/vsc31305/ray_spill',
                    importance_scoring_method = 'GBM',
                    importance_scoring_kwargs = GBM_KWARGS)
2022-01-04 17:59:17,845 R2G          INFO     Calculating region to gene importances, using GBM method
2022-01-04 17:59:30,930 INFO services.py:1338 -- View the Ray dashboard at http://127.0.0.1:8267
initializing:  22%|██▏       | 3225/14548 [04:27<15:17, 12.33it/s]
(score_regions_to_single_gene_ray pid=34847)
initializing:  27%|██▋       | 3905/14548 [05:23<14:24, 12.30it/s]
(score_regions_to_single_gene_ray pid=34848)
initializing:  27%|██▋       | 3953/14548 [05:27<14:34, 12.11it/s]
(score_regions_to_single_gene_ray pid=34840)
initializing:  54%|█████▍    | 7836/14548 [11:03<09:18, 12.01it/s]  (score_regions_to_single_gene_ray pid=34844)
initializing:  68%|██████▊   | 9874/14548 [13:49<06:23, 12.20it/s]
(score_regions_to_single_gene_ray pid=34855)
initializing: 100%|██████████| 14548/14548 [20:26<00:00, 11.86it/s]
Running using 20 cores: 100%|██████████| 14548/14548 [00:35<00:00, 408.74it/s]
2022-01-04 18:20:38,987 R2G          INFO     Took 1281.1407558918 seconds
2022-01-04 18:20:38,990 R2G          INFO     Calculating region to gene correlation, using SR method
2022-01-04 18:20:53,862 INFO services.py:1338 -- View the Ray dashboard at http://127.0.0.1:8267
initializing:  68%|██████▊   | 9871/14548 [13:15<06:17, 12.39it/s]  (score_regions_to_single_gene_ray pid=20985)
initializing:  84%|████████▍ | 12185/14548 [16:17<03:03, 12.88it/s](score_regions_to_single_gene_ray pid=20982)
initializing:  98%|█████████▊| 14275/14548 [19:01<00:21, 12.94it/s](score_regions_to_single_gene_ray pid=20988)
initializing: 100%|██████████| 14548/14548 [19:23<00:00, 12.51it/s]
Running using 20 cores: 100%|██████████| 14548/14548 [00:50<00:00, 288.45it/s]
2022-01-04 18:41:13,309 R2G          INFO     Took 1234.3179178237915 seconds
2022-01-04 18:41:26,108 R2G          INFO     Done!
[32]:
# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  pickle.dump(scplus_obj, f)

5. Infer TF to gene relationships

The next step is to infer relationships between TFs and target genes based on expression. We will use similar approaches as for the enhancer-to-gene relationships (GBM/RF and correlation).

[33]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[ ]:
from scenicplus.TF_to_gene import *
tf_file = '/staging/leuven/stg_00002/lcb/cflerin/resources/allTFs_hg38.txt'
calculate_TFs_to_genes_relationships(scplus_obj,
                    tf_file = tf_file,
                    ray_n_cpu = 20,
                    method = 'GBM',
                    _temp_dir = '/scratch/leuven/313/vsc31305/ray_spill',
                    key= 'TF2G_adj')
[ ]:
# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  pickle.dump(scplus_obj, f)

If you have run SCENIC before in the gene expression matrix, it is possible to directly load adjancencies from that pipeline:

[ ]:
load_TF2G_adj_from_file(scplus_obj,
                        f_adj = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/rna/vsn/single_sample_scenic_HQ/out/scenic/10x_multiome_brain_HQ/arboreto_with_multiprocessing/10x_multiome_brain_HQ__adj.tsv',
                        inplace = True,
                        key= 'TF2G_adj')

6. Build eGRNs

The last step is to build the eGRNs using a recovery approach (GSEA). The ranking to use will be based on the TF-2-gene importances, while gene sets will be derived with different thresholding methods on the enhancer-to-gene relationships and the unfiltered cistromes.

[1]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[ ]:
# Load functions
from scenicplus.grn_builder.gsea_approach import build_grn

build_grn(scplus_obj,
         min_target_genes = 10,
         adj_pval_thr = 1,
         min_regions_per_gene = 0,
         quantiles = (0.85, 0.90, 0.95),
         top_n_regionTogenes_per_gene = (5, 10, 15),
         top_n_regionTogenes_per_region = (),
         binarize_using_basc = True,
         rho_dichotomize_tf2g = True,
         rho_dichotomize_r2g = True,
         rho_dichotomize_eregulon = True,
         rho_threshold = 0.05,
         keep_extended_motif_annot = True,
         merge_eRegulons = True,
         order_regions_to_genes_by = 'importance',
         order_TFs_to_genes_by = 'importance',
         key_added = 'eRegulons_importance',
         cistromes_key = 'Unfiltered',
         disable_tqdm = False, #If running in notebook, set to True
         ray_n_cpu = 20,
         _temp_dir = '/scratch/leuven/313/vsc31305/ray_spill')

To access the eGRNs:

[ ]:
import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

7. Exploring SCENIC+ results

A. Generate eRegulon metadata

As a first step, we will format the eGRN metadata. This will integrate the results from the inference of enhancer-to-gene and TF-to-gene relationships and the eRegulon construction in one pandas dataframe that can be used for further exploration.

[2]:
import dill
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = dill.load(infile)
infile.close()
[3]:
from scenicplus.utils import format_egrns
format_egrns(scplus_obj, eregulons_key = 'eRegulons_importance', TF2G_key = 'TF2G_adj', key_added = 'eRegulon_metadata')

The eRegulon metadata will look as:

[4]:
scplus_obj.uns['eRegulon_metadata'][0:10]
[4]:
Region_signature_name Gene_signature_name TF is_extended Region Gene R2G_importance R2G_rho R2G_importance_x_rho R2G_importance_x_abs_rho TF2G_importance TF2G_regulation TF2G_rho TF2G_importance_x_abs_rho TF2G_importance_x_rho
0 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr1:244918651-244919151 HNRNPU 0.021839 0.085878 0.001876 0.001876 0.236510 1 0.122128 0.028884 0.028884
1 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr10:88991114-88991614 FAS 0.023378 0.215477 0.005037 0.005037 0.457794 1 0.162854 0.074554 0.074554
2 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr10:88990599-88991099 FAS 0.031784 0.212687 0.006760 0.006760 0.457794 1 0.162854 0.074554 0.074554
3 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chrX:107676906-107677406 MID2 0.021056 0.098110 0.002066 0.002066 0.663257 1 0.165609 0.109842 0.109842
4 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr16:1556214-1556714 IFT140 0.015752 0.098841 0.001557 0.001557 0.336581 1 0.095788 0.032240 0.032240
5 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr10:103692953-103693453 SH3PXD2A 0.004005 0.146456 0.000587 0.000587 4.731638 1 0.200345 0.947960 0.947960
6 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr19:57527137-57527637 ZNF549 0.078793 0.083988 0.006618 0.006618 0.522762 1 0.154494 0.080764 0.080764
7 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr22:36323698-36324198 TXN2 0.020686 0.073909 0.001529 0.001529 1.298719 1 0.160698 0.208702 0.208702
8 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr6:15365250-15365750 JARID2 0.025076 0.174829 0.004384 0.004384 0.678238 1 0.231925 0.157300 0.157300
9 ACO1_+_+_(24r) ACO1_+_+_(23g) ACO1 False chr10:126525383-126525883 ADAM12 0.027865 0.067947 0.001893 0.001893 0.233867 1 0.090295 0.021117 0.021117

The first sign in the eRegulon name indicates the relationship between TF and gene; while the second indicates the relationship between region and gene. Additional columns can be added by the user, for example the enrichment of the TF motif in the regions.

B. Assesing eGRN enrichment in cells

Next, we can score the eRegulons in the cells to assess how enriched target regions and target genes are enriched in each cell. We will score the region-based regulons on the scATAC-seq layer, while gene-based eRegulons will be scored using the transcriptome layer.

[40]:
# Format eRegulons
from scenicplus.eregulon_enrichment import *
get_eRegulons_as_signatures(scplus_obj, eRegulon_metadata_key='eRegulon_metadata', key_added='eRegulon_signatures')
[6]:
## Score chromatin layer
# Region based raking
from scenicplus.cistromes import *
import time
start_time = time.time()
region_ranking = make_rankings(scplus_obj, target='region')
# Score region regulons
score_eRegulons(scplus_obj,
                ranking = region_ranking,
                eRegulon_signatures_key = 'eRegulon_signatures',
                key_added = 'eRegulon_AUC',
                enrichment_type= 'region',
                auc_threshold = 0.05,
                normalize = False,
                n_cpu = 1)
time = time.time()-start_time
print(time/60)
100%|██████████| 832/832 [00:26<00:00, 31.16it/s]
2.8363844593365988
[7]:
## Score transcriptome layer
# Gene based raking
from scenicplus.cistromes import *
import time
start_time = time.time()
gene_ranking = make_rankings(scplus_obj, target='gene')
# Score gene regulons
score_eRegulons(scplus_obj,
                gene_ranking,
                eRegulon_signatures_key = 'eRegulon_signatures',
                key_added = 'eRegulon_AUC',
                enrichment_type = 'gene',
                auc_threshold = 0.05,
                normalize= False,
                n_cpu = 1)
time = time.time()-start_time
print(time/60)
100%|██████████| 832/832 [00:07<00:00, 104.80it/s]
0.21863729159037273

Next we can assess the relationship between the TF expression and the eRegulons; in other words, whether genes expressed/repressed and regions accessible/closed when the TF is present. Due to the amount of drop-outs, and the variability in cell types proportions, using directly the AUC cistrome matrix can result in noisy correlations. Here, we use pseudobulks, in which we sample a number of cells per cell type. In this example, we merge 5 cells per pseudobulk and generate 100 pseudobulks per cell type.

[8]:
# Generate pseudobulks
import time
start_time = time.time()
generate_pseudobulks(scplus_obj,
                         variable = 'ACC_Seurat_cell_type',
                         auc_key = 'eRegulon_AUC',
                         signature_key = 'Gene_based',
                         nr_cells = 5,
                         nr_pseudobulks = 100,
                         seed=555)
generate_pseudobulks(scplus_obj,
                         variable = 'ACC_Seurat_cell_type',
                         auc_key = 'eRegulon_AUC',
                         signature_key = 'Region_based',
                         nr_cells = 5,
                         nr_pseudobulks = 100,
                         seed=555)
time = time.time()-start_time
print(time/60)
1.9503313183784485
[9]:
# Correlation between TF and eRegulons
import time
start_time = time.time()
TF_cistrome_correlation(scplus_obj,
                        variable = 'ACC_Seurat_cell_type',
                        auc_key = 'eRegulon_AUC',
                        signature_key = 'Gene_based',
                        out_key = 'ACC_Seurat_cell_type_eGRN_gene_based')
TF_cistrome_correlation(scplus_obj,
                        variable = 'ACC_Seurat_cell_type',
                        auc_key = 'eRegulon_AUC',
                        signature_key = 'Region_based',
                        out_key = 'ACC_Seurat_cell_type_eGRN_region_based')
time = time.time()-start_time
print(time/60)
0.032043548425038655

We observe that positively correlated gene-based regulons show a positive TF-gene correlation as well (first sign in the name), while negatively correlated ones show a negative TF-gene correlation.

[10]:
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.float_format', lambda x: '%.5f' % x)
display(scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_gene_based'].sort_values('Rho', ascending=False))
TF Cistrome Rho P-value Adjusted_p-value
557 RFX4 RFX4_+_-_(37g) 0.92784 0.00000 0.00000
253 IKZF1 IKZF1_+_+_(265g) 0.92577 0.00000 0.00000
255 IKZF1 IKZF1_extended_+_+_(265g) 0.92577 0.00000 0.00000
560 RFX4 RFX4_extended_+_-_(43g) 0.92386 0.00000 0.00000
172 FLI1 FLI1_+_+_(274g) 0.89842 0.00000 0.00000
174 FLI1 FLI1_extended_+_+_(290g) 0.89731 0.00000 0.00000
41 BCL11A BCL11A_+_+_(874g) 0.89626 0.00000 0.00000
42 BCL11A BCL11A_extended_+_+_(874g) 0.89626 0.00000 0.00000
269 IRF8 IRF8_+_+_(165g) 0.89555 0.00000 0.00000
270 IRF8 IRF8_extended_+_+_(165g) 0.89555 0.00000 0.00000
575 RUNX1 RUNX1_extended_+_+_(54g) 0.89246 0.00000 0.00000
574 RUNX1 RUNX1_+_+_(54g) 0.89246 0.00000 0.00000
638 SOX5 SOX5_+_-_(656g) 0.88983 0.00000 0.00000
641 SOX5 SOX5_extended_+_-_(705g) 0.88853 0.00000 0.00000
754 THRB THRB_+_+_(198g) 0.88745 0.00000 0.00000
756 THRB THRB_extended_+_+_(198g) 0.88745 0.00000 0.00000
459 OLIG2 OLIG2_+_+_(102g) 0.88460 0.00000 0.00000
461 OLIG2 OLIG2_extended_+_+_(95g) 0.88448 0.00000 0.00000
145 EMX2 EMX2_extended_+_+_(260g) 0.88445 0.00000 0.00000
144 EMX2 EMX2_+_+_(260g) 0.88346 0.00000 0.00000
363 MEF2C MEF2C_extended_+_-_(203g) 0.87798 0.00000 0.00000
106 EBF1 EBF1_+_-_(16g) 0.86981 0.00000 0.00000
107 EBF1 EBF1_extended_+_-_(16g) 0.86981 0.00000 0.00000
360 MEF2C MEF2C_+_-_(159g) 0.86918 0.00000 0.00000
328 LEF1 LEF1_extended_+_+_(17g) 0.86885 0.00000 0.00000
359 MEF2C MEF2C_+_+_(1761g) 0.86788 0.00000 0.00000
362 MEF2C MEF2C_extended_+_+_(1788g) 0.86694 0.00000 0.00000
458 OLIG1 OLIG1_extended_+_-_(16g) 0.86512 0.00000 0.00000
457 OLIG1 OLIG1_+_-_(16g) 0.86512 0.00000 0.00000
825 ZNF536 ZNF536_+_+_(143g) 0.86114 0.00000 0.00000
827 ZNF536 ZNF536_extended_+_+_(143g) 0.86114 0.00000 0.00000
327 LEF1 LEF1_+_+_(18g) 0.85916 0.00000 0.00000
254 IKZF1 IKZF1_+_-_(31g) 0.85384 0.00000 0.00000
256 IKZF1 IKZF1_extended_+_-_(31g) 0.85384 0.00000 0.00000
441 NKX6-2 NKX6-2_extended_+_-_(127g) 0.84669 0.00000 0.00000
439 NKX6-2 NKX6-2_+_-_(117g) 0.84500 0.00000 0.00000
466 PAX6 PAX6_extended_+_+_(163g) 0.83567 0.00000 0.00000
755 THRB THRB_+_-_(112g) 0.83272 0.00000 0.00000
757 THRB THRB_extended_+_-_(112g) 0.83272 0.00000 0.00000
13 ARNTL2 ARNTL2_extended_+_+_(10g) 0.83159 0.00000 0.00000
12 ARNTL2 ARNTL2_+_+_(10g) 0.83159 0.00000 0.00000
709 TAL1 TAL1_+_+_(99g) 0.83009 0.00000 0.00000
710 TAL1 TAL1_extended_+_+_(103g) 0.82871 0.00000 0.00000
334 LHX6 LHX6_extended_+_-_(32g) 0.82743 0.00000 0.00000
465 PAX6 PAX6_+_+_(40g) 0.82422 0.00000 0.00000
596 SOX10 SOX10_+_+_(209g) 0.81895 0.00000 0.00000
598 SOX10 SOX10_extended_+_+_(209g) 0.81895 0.00000 0.00000
200 FOXO1 FOXO1_extended_+_+_(97g) 0.81784 0.00000 0.00000
197 FOXO1 FOXO1_+_+_(97g) 0.81784 0.00000 0.00000
18 ARX ARX_+_+_(54g) 0.81727 0.00000 0.00000
393 NEUROD2 NEUROD2_extended_+_+_(46g) 0.81676 0.00000 0.00000
25 ASCL1 ASCL1_extended_+_-_(76g) 0.81562 0.00000 0.00000
467 PAX6 PAX6_extended_+_-_(18g) 0.81378 0.00000 0.00000
462 OLIG2 OLIG2_extended_+_-_(46g) 0.81113 0.00000 0.00000
460 OLIG2 OLIG2_+_-_(46g) 0.81113 0.00000 0.00000
333 LHX6 LHX6_extended_+_+_(131g) 0.81009 0.00000 0.00000
331 LHX6 LHX6_+_+_(60g) 0.80462 0.00000 0.00000
518 PRRX1 PRRX1_extended_+_+_(220g) 0.80268 0.00000 0.00000
515 PRRX1 PRRX1_+_+_(207g) 0.80255 0.00000 0.00000
664 SOX9 SOX9_extended_+_-_(229g) 0.79496 0.00000 0.00000
194 FOXN2 FOXN2_extended_+_+_(70g) 0.79484 0.00000 0.00000
661 SOX9 SOX9_+_-_(224g) 0.79432 0.00000 0.00000
4 ADARB1 ADARB1_+_+_(20g) 0.79315 0.00000 0.00000
5 ADARB1 ADARB1_extended_+_+_(20g) 0.79315 0.00000 0.00000
392 NEUROD2 NEUROD2_+_+_(38g) 0.79257 0.00000 0.00000
635 SOX4 SOX4_extended_+_-_(151g) 0.79080 0.00000 0.00000
660 SOX9 SOX9_+_+_(58g) 0.78950 0.00000 0.00000
632 SOX4 SOX4_+_-_(145g) 0.78767 0.00000 0.00000
663 SOX9 SOX9_extended_+_+_(60g) 0.78753 0.00000 0.00000
21 ASCL1 ASCL1_+_+_(75g) 0.78552 0.00000 0.00000
22 ASCL1 ASCL1_+_-_(50g) 0.77859 0.00000 0.00000
121 ELF1 ELF1_extended_+_+_(264g) 0.77623 0.00000 0.00000
117 ELF1 ELF1_+_+_(263g) 0.77552 0.00000 0.00000
198 FOXO1 FOXO1_+_-_(148g) 0.77109 0.00000 0.00000
143 EMX1 EMX1_extended_+_-_(34g) 0.76930 0.00000 0.00000
201 FOXO1 FOXO1_extended_+_-_(146g) 0.76864 0.00000 0.00000
389 NCALD NCALD_extended_+_-_(19g) 0.76585 0.00000 0.00000
388 NCALD NCALD_+_-_(19g) 0.76585 0.00000 0.00000
73 CREB5 CREB5_extended_+_+_(29g) 0.76343 0.00000 0.00000
72 CREB5 CREB5_+_+_(29g) 0.76343 0.00000 0.00000
381 MYEF2 MYEF2_+_+_(63g) 0.76109 0.00000 0.00000
24 ASCL1 ASCL1_extended_+_+_(100g) 0.75666 0.00000 0.00000
696 STAT4 STAT4_extended_+_+_(21g) 0.75615 0.00000 0.00000
695 STAT4 STAT4_+_+_(21g) 0.75615 0.00000 0.00000
382 MYEF2 MYEF2_extended_+_+_(645g) 0.75053 0.00000 0.00000
329 LHX2 LHX2_+_+_(275g) 0.75016 0.00000 0.00000
542 REL REL_extended_+_+_(133g) 0.74990 0.00000 0.00000
180 FOSL2 FOSL2_extended_+_-_(44g) 0.74966 0.00000 0.00000
330 LHX2 LHX2_extended_+_+_(302g) 0.74854 0.00000 0.00000
176 FOSB FOSB_+_-_(51g) 0.74768 0.00000 0.00000
178 FOSB FOSB_extended_+_-_(51g) 0.74768 0.00000 0.00000
296 KLF12 KLF12_extended_+_+_(138g) 0.74734 0.00000 0.00000
734 TCF7L1 TCF7L1_extended_+_-_(16g) 0.74084 0.00000 0.00000
733 TCF7L1 TCF7L1_+_-_(15g) 0.73833 0.00000 0.00000
794 ZEB1 ZEB1_extended_+_+_(19g) 0.73368 0.00000 0.00000
793 ZEB1 ZEB1_+_+_(19g) 0.73368 0.00000 0.00000
438 NKX6-2 NKX6-2_+_+_(25g) 0.73318 0.00000 0.00000
440 NKX6-2 NKX6-2_extended_+_+_(26g) 0.72926 0.00000 0.00000
237 HES1 HES1_extended_+_-_(38g) 0.72513 0.00000 0.00000
179 FOSL2 FOSL2_+_-_(33g) 0.72469 0.00000 0.00000
142 EMX1 EMX1_+_-_(25g) 0.72438 0.00000 0.00000
735 TCF7L2 TCF7L2_+_+_(84g) 0.71966 0.00000 0.00000
738 TCF7L2 TCF7L2_extended_+_+_(88g) 0.71907 0.00000 0.00000
277 JUNB JUNB_+_-_(80g) 0.71763 0.00000 0.00000
602 SOX11 SOX11_extended_+_-_(120g) 0.71719 0.00000 0.00000
186 FOXG1 FOXG1_extended_+_+_(36g) 0.71560 0.00000 0.00000
332 LHX6 LHX6_+_-_(35g) 0.71345 0.00000 0.00000
600 SOX11 SOX11_+_-_(89g) 0.71165 0.00000 0.00000
181 FOS FOS_+_-_(126g) 0.71158 0.00000 0.00000
183 FOS FOS_extended_+_-_(126g) 0.71158 0.00000 0.00000
325 LARP1 LARP1_+_+_(114g) 0.70879 0.00000 0.00000
326 LARP1 LARP1_extended_+_+_(114g) 0.70879 0.00000 0.00000
532 RARB RARB_extended_+_+_(100g) 0.70495 0.00000 0.00000
531 RARB RARB_+_+_(100g) 0.70495 0.00000 0.00000
279 JUNB JUNB_extended_+_-_(84g) 0.70189 0.00000 0.00000
547 RFX1 RFX1_extended_+_+_(364g) 0.70040 0.00000 0.00000
507 POU6F2 POU6F2_extended_+_+_(309g) 0.69929 0.00000 0.00000
506 POU6F2 POU6F2_+_+_(309g) 0.69929 0.00000 0.00000
185 FOXG1 FOXG1_+_+_(17g) 0.69909 0.00000 0.00000
657 SOX8 SOX8_extended_+_+_(104g) 0.69230 0.00000 0.00000
654 SOX8 SOX8_+_+_(96g) 0.69045 0.00000 0.00000
739 TCF7L2 TCF7L2_extended_+_-_(57g) 0.68803 0.00000 0.00000
736 TCF7L2 TCF7L2_+_-_(48g) 0.68551 0.00000 0.00000
545 RFX1 RFX1_+_+_(326g) 0.68514 0.00000 0.00000
30 ATF3 ATF3_extended_+_+_(77g) 0.68467 0.00000 0.00000
648 SOX6 SOX6_extended_+_-_(440g) 0.68271 0.00000 0.00000
644 SOX6 SOX6_+_-_(488g) 0.68187 0.00000 0.00000
310 KLF4 KLF4_+_+_(28g) 0.68007 0.00000 0.00000
311 KLF4 KLF4_extended_+_+_(28g) 0.68007 0.00000 0.00000
576 RXRA RXRA_+_+_(308g) 0.67837 0.00000 0.00000
578 RXRA RXRA_extended_+_+_(308g) 0.67837 0.00000 0.00000
29 ATF3 ATF3_+_+_(74g) 0.67772 0.00000 0.00000
90 DLX1 DLX1_+_-_(26g) 0.67206 0.00000 0.00000
617 SOX1 SOX1_extended_+_+_(47g) 0.67151 0.00000 0.00000
162 ETV1 ETV1_extended_+_+_(61g) 0.66922 0.00000 0.00000
92 DLX1 DLX1_extended_+_-_(32g) 0.66883 0.00000 0.00000
555 RFX3 RFX3_extended_+_+_(686g) 0.66633 0.00000 0.00000
565 RFX8 RFX8_extended_+_+_(147g) 0.66548 0.00000 0.00000
553 RFX3 RFX3_+_+_(561g) 0.66377 0.00000 0.00000
713 TCF12 TCF12_+_+_(665g) 0.66312 0.00000 0.00000
717 TCF12 TCF12_extended_+_+_(693g) 0.66284 0.00000 0.00000
368 MEIS2 MEIS2_extended_+_+_(22g) 0.65962 0.00000 0.00000
579 RXRA RXRA_extended_+_-_(150g) 0.65410 0.00000 0.00000
577 RXRA RXRA_+_-_(150g) 0.65410 0.00000 0.00000
91 DLX1 DLX1_extended_+_+_(129g) 0.65253 0.00000 0.00000
89 DLX1 DLX1_+_+_(127g) 0.65177 0.00000 0.00000
725 TCF4 TCF4_+_+_(551g) 0.64794 0.00000 0.00000
175 FOSB FOSB_+_+_(57g) 0.64500 0.00000 0.00000
177 FOSB FOSB_extended_+_+_(57g) 0.64500 0.00000 0.00000
390 NEUROD1 NEUROD1_+_+_(66g) 0.64294 0.00000 0.00000
391 NEUROD1 NEUROD1_extended_+_+_(66g) 0.64294 0.00000 0.00000
729 TCF4 TCF4_extended_+_+_(783g) 0.64190 0.00000 0.00000
340 MAFF MAFF_extended_+_+_(23g) 0.64131 0.00000 0.00000
339 MAFF MAFF_+_+_(22g) 0.64126 0.00000 0.00000
163 ETV1 ETV1_extended_+_-_(47g) 0.64107 0.00000 0.00000
399 NFAT5 NFAT5_extended_+_+_(159g) 0.64058 0.00000 0.00000
396 NFAT5 NFAT5_+_+_(159g) 0.64058 0.00000 0.00000
238 HES4 HES4_extended_+_-_(10g) 0.63934 0.00000 0.00000
313 KLF6 KLF6_+_+_(117g) 0.63888 0.00000 0.00000
314 KLF6 KLF6_extended_+_+_(117g) 0.63888 0.00000 0.00000
422 NFIB NFIB_extended_+_+_(574g) 0.63562 0.00000 0.00000
421 NFIB NFIB_+_+_(574g) 0.63562 0.00000 0.00000
118 ELF1 ELF1_+_-_(151g) 0.63526 0.00000 0.00000
584 SALL3 SALL3_extended_+_+_(10g) 0.63498 0.00000 0.00000
409 NFE2L2 NFE2L2_+_-_(98g) 0.63482 0.00000 0.00000
503 POU3F4 POU3F4_extended_+_+_(62g) 0.63478 0.00000 0.00000
549 RFX2 RFX2_+_-_(38g) 0.63441 0.00000 0.00000
350 MCTP2 MCTP2_extended_+_+_(66g) 0.63306 0.00000 0.00000
349 MCTP2 MCTP2_+_+_(66g) 0.63306 0.00000 0.00000
551 RFX2 RFX2_extended_+_-_(36g) 0.63241 0.00000 0.00000
492 POU2F1 POU2F1_+_+_(287g) 0.63192 0.00000 0.00000
122 ELF1 ELF1_extended_+_-_(156g) 0.63106 0.00000 0.00000
411 NFE2L2 NFE2L2_extended_+_-_(120g) 0.62626 0.00000 0.00000
509 PRDM1 PRDM1_+_+_(77g) 0.62545 0.00000 0.00000
510 PRDM1 PRDM1_extended_+_+_(77g) 0.62545 0.00000 0.00000
502 POU3F4 POU3F4_+_+_(53g) 0.62533 0.00000 0.00000
293 KLF12 KLF12_+_-_(50g) 0.62394 0.00000 0.00000
297 KLF12 KLF12_extended_+_-_(51g) 0.62384 0.00000 0.00000
714 TCF12 TCF12_+_-_(250g) 0.62001 0.00000 0.00000
718 TCF12 TCF12_extended_+_-_(265g) 0.61947 0.00000 0.00000
289 JUN JUN_extended_+_-_(64g) 0.61710 0.00000 0.00000
112 EGR4 EGR4_extended_+_+_(187g) 0.61644 0.00000 0.00000
158 ETS2 ETS2_+_+_(283g) 0.61288 0.00000 0.00000
159 ETS2 ETS2_extended_+_+_(284g) 0.61285 0.00000 0.00000
631 SOX4 SOX4_+_+_(57g) 0.60937 0.00000 0.00000
161 ETV1 ETV1_+_-_(34g) 0.60873 0.00000 0.00000
826 ZNF536 ZNF536_+_-_(23g) 0.60551 0.00000 0.00000
828 ZNF536 ZNF536_extended_+_-_(23g) 0.60551 0.00000 0.00000
169 ETV6 ETV6_+_-_(42g) 0.60227 0.00000 0.00000
287 JUN JUN_+_-_(68g) 0.60043 0.00000 0.00000
160 ETV1 ETV1_+_+_(44g) 0.60034 0.00000 0.00000
417 NFIA NFIA_extended_+_+_(483g) 0.59964 0.00000 0.00000
413 NFIA NFIA_+_+_(483g) 0.59964 0.00000 0.00000
111 EGR4 EGR4_+_+_(135g) 0.59786 0.00000 0.00000
516 PRRX1 PRRX1_+_-_(31g) 0.59786 0.00000 0.00000
407 NFE2L1 NFE2L1_extended_+_+_(334g) 0.59390 0.00000 0.00000
634 SOX4 SOX4_extended_+_+_(72g) 0.59381 0.00000 0.00000
473 PBX3 PBX3_extended_+_+_(128g) 0.59353 0.00000 0.00000
472 PBX3 PBX3_+_+_(128g) 0.59353 0.00000 0.00000
618 SOX1 SOX1_extended_+_-_(116g) 0.59338 0.00000 0.00000
338 MAFB MAFB_extended_+_-_(12g) 0.59291 0.00000 0.00000
787 ZBTB7A ZBTB7A_+_+_(32g) 0.59184 0.00000 0.00000
789 ZBTB7A ZBTB7A_extended_+_+_(32g) 0.59184 0.00000 0.00000
406 NFE2L1 NFE2L1_+_+_(313g) 0.58886 0.00000 0.00000
79 CUX1 CUX1_+_+_(247g) 0.58873 0.00000 0.00000
82 CUX1 CUX1_extended_+_+_(247g) 0.58873 0.00000 0.00000
760 TPPP TPPP_extended_+_+_(64g) 0.58850 0.00000 0.00000
758 TPPP TPPP_+_+_(64g) 0.58850 0.00000 0.00000
519 PRRX1 PRRX1_extended_+_-_(33g) 0.58823 0.00000 0.00000
196 FOXN3 FOXN3_extended_+_+_(71g) 0.58527 0.00000 0.00000
676 SPI1 SPI1_+_+_(213g) 0.58211 0.00000 0.00000
678 SPI1 SPI1_extended_+_+_(219g) 0.58112 0.00000 0.00000
620 SOX2 SOX2_+_+_(52g) 0.58048 0.00000 0.00000
764 TRPS1 TRPS1_extended_+_+_(16g) 0.57907 0.00000 0.00000
624 SOX2 SOX2_extended_+_-_(111g) 0.57718 0.00000 0.00000
679 SPI1 SPI1_extended_+_-_(38g) 0.57663 0.00000 0.00000
337 MAFB MAFB_+_-_(11g) 0.57585 0.00000 0.00000
781 ZBTB18 ZBTB18_+_-_(150g) 0.57548 0.00000 0.00000
623 SOX2 SOX2_extended_+_+_(54g) 0.57402 0.00000 0.00000
616 SOX1 SOX1_+_-_(103g) 0.57316 0.00000 0.00000
15 ARNTL ARNTL_extended_+_+_(18g) 0.56042 0.00000 0.00000
96 DLX6 DLX6_extended_+_+_(36g) 0.56036 0.00000 0.00000
782 ZBTB18 ZBTB18_extended_+_-_(172g) 0.55973 0.00000 0.00000
493 POU2F1 POU2F1_extended_+_+_(456g) 0.55929 0.00000 0.00000
414 NFIA NFIA_+_-_(255g) 0.55911 0.00000 0.00000
418 NFIA NFIA_extended_+_-_(255g) 0.55911 0.00000 0.00000
640 SOX5 SOX5_extended_+_+_(91g) 0.55762 0.00000 0.00000
446 NR2E1 NR2E1_+_+_(62g) 0.55339 0.00000 0.00000
601 SOX11 SOX11_extended_+_+_(21g) 0.55224 0.00000 0.00000
608 SOX13 SOX13_extended_+_-_(42g) 0.55206 0.00000 0.00000
742 TEAD1 TEAD1_extended_+_+_(265g) 0.54845 0.00000 0.00000
740 TEAD1 TEAD1_+_+_(265g) 0.54845 0.00000 0.00000
14 ARNTL ARNTL_+_+_(23g) 0.54649 0.00000 0.00000
95 DLX6 DLX6_+_+_(33g) 0.54551 0.00000 0.00000
312 KLF5 KLF5_extended_+_-_(28g) 0.54289 0.00000 0.00000
170 ETV6 ETV6_extended_+_+_(366g) 0.54179 0.00000 0.00000
369 MEIS2 MEIS2_extended_+_-_(67g) 0.53723 0.00000 0.00000
769 VSX1 VSX1_+_+_(38g) 0.53444 0.00000 0.00000
770 VSX1 VSX1_extended_+_+_(38g) 0.53444 0.00000 0.00000
168 ETV6 ETV6_+_+_(358g) 0.53125 0.00000 0.00000
425 NFIC NFIC_extended_+_+_(911g) 0.52577 0.00000 0.00000
423 NFIC NFIC_+_+_(911g) 0.52577 0.00000 0.00000
408 NFE2L1 NFE2L1_extended_+_-_(21g) 0.52570 0.00000 0.00000
187 FOXG1 FOXG1_extended_+_-_(122g) 0.52558 0.00000 0.00000
522 PRRX2 PRRX2_extended_+_+_(21g) 0.52451 0.00000 0.00000
521 PRRX2 PRRX2_+_+_(21g) 0.52451 0.00000 0.00000
249 HOXD1 HOXD1_+_-_(20g) 0.52146 0.00000 0.00000
795 ZEB2 ZEB2_+_-_(21g) 0.51567 0.00000 0.00000
797 ZEB2 ZEB2_extended_+_-_(21g) 0.51567 0.00000 0.00000
431 NFIX NFIX_extended_+_-_(499g) 0.51384 0.00000 0.00000
428 NFIX NFIX_+_-_(499g) 0.51384 0.00000 0.00000
525 PURA PURA_extended_+_-_(203g) 0.51219 0.00000 0.00000
523 PURA PURA_+_-_(203g) 0.51219 0.00000 0.00000
447 NR2E1 NR2E1_extended_+_+_(68g) 0.51127 0.00000 0.00000
765 VEZF1 VEZF1_+_-_(54g) 0.51100 0.00000 0.00000
767 VEZF1 VEZF1_extended_+_-_(54g) 0.51100 0.00000 0.00000
726 TCF4 TCF4_+_-_(310g) 0.50194 0.00000 0.00000
730 TCF4 TCF4_extended_+_-_(359g) 0.49968 0.00000 0.00000
251 HOXD1 HOXD1_extended_+_-_(25g) 0.49833 0.00000 0.00000
58 CEBPD CEBPD_extended_+_+_(47g) 0.49753 0.00000 0.00000
57 CEBPD CEBPD_+_+_(47g) 0.49753 0.00000 0.00000
595 SMARCC2 SMARCC2_extended_+_+_(53g) 0.49727 0.00000 0.00000
594 SMARCC2 SMARCC2_+_+_(53g) 0.49727 0.00000 0.00000
211 FOXP2 FOXP2_extended_+_+_(36g) 0.49687 0.00000 0.00000
209 FOXP2 FOXP2_+_+_(36g) 0.49687 0.00000 0.00000
607 SOX13 SOX13_extended_+_+_(37g) 0.49666 0.00000 0.00000
677 SPI1 SPI1_+_-_(17g) 0.49036 0.00000 0.00000
241 HMX1 HMX1_+_+_(30g) 0.48301 0.00000 0.00000
242 HMX1 HMX1_extended_+_+_(30g) 0.48301 0.00000 0.00000
35 BACH1 BACH1_extended_+_-_(85g) 0.48004 0.00000 0.00000
32 ATF7 ATF7_extended_+_+_(18g) 0.47959 0.00000 0.00000
243 HNF4G HNF4G_+_+_(12g) 0.47956 0.00000 0.00000
245 HNF4G HNF4G_extended_+_+_(12g) 0.47956 0.00000 0.00000
823 ZNF467 ZNF467_+_+_(38g) 0.47614 0.00000 0.00000
128 ELF2 ELF2_extended_+_-_(302g) 0.46920 0.00000 0.00000
20 AR AR_extended_+_-_(35g) 0.46873 0.00000 0.00000
19 AR AR_+_-_(35g) 0.46873 0.00000 0.00000
31 ATF7 ATF7_+_+_(16g) 0.46640 0.00000 0.00000
605 SOX13 SOX13_+_+_(35g) 0.46499 0.00000 0.00000
234 HDAC2 HDAC2_extended_+_-_(39g) 0.46461 0.00000 0.00000
232 HDAC2 HDAC2_+_-_(39g) 0.46461 0.00000 0.00000
148 EP300 EP300_extended_+_+_(925g) 0.46396 0.00000 0.00000
147 EP300 EP300_+_+_(925g) 0.46396 0.00000 0.00000
543 REST REST_+_+_(30g) 0.46342 0.00000 0.00000
125 ELF2 ELF2_+_-_(278g) 0.46034 0.00000 0.00000
152 ERG ERG_extended_+_+_(43g) 0.45929 0.00000 0.00000
427 NFIX NFIX_+_+_(210g) 0.45866 0.00000 0.00000
430 NFIX NFIX_extended_+_+_(210g) 0.45866 0.00000 0.00000
171 ETV6 ETV6_extended_+_-_(57g) 0.45857 0.00000 0.00000
544 REST REST_extended_+_+_(43g) 0.45763 0.00000 0.00000
228 HBP1 HBP1_extended_+_+_(203g) 0.45642 0.00000 0.00000
400 NFAT5 NFAT5_extended_+_-_(64g) 0.45562 0.00000 0.00000
397 NFAT5 NFAT5_+_-_(64g) 0.45562 0.00000 0.00000
141 EMX1 EMX1_+_+_(17g) 0.45494 0.00000 0.00000
300 KLF13 KLF13_+_-_(80g) 0.45488 0.00000 0.00000
301 KLF13 KLF13_extended_+_-_(80g) 0.45488 0.00000 0.00000
227 HBP1 HBP1_+_+_(173g) 0.45474 0.00000 0.00000
495 POU2F2 POU2F2_extended_+_+_(75g) 0.45437 0.00000 0.00000
306 KLF2 KLF2_+_+_(17g) 0.45384 0.00000 0.00000
307 KLF2 KLF2_extended_+_+_(17g) 0.45384 0.00000 0.00000
151 ERG ERG_+_+_(40g) 0.45136 0.00000 0.00000
831 ZNF736 ZNF736_+_+_(61g) 0.45110 0.00000 0.00000
376 MSX1 MSX1_extended_+_+_(76g) 0.44952 0.00000 0.00000
374 MSX1 MSX1_+_+_(72g) 0.44695 0.00000 0.00000
157 ETS1 ETS1_extended_+_-_(143g) 0.44398 0.00000 0.00000
452 NRF1 NRF1_+_+_(299g) 0.44383 0.00000 0.00000
155 ETS1 ETS1_+_-_(142g) 0.44318 0.00000 0.00000
599 SOX10 SOX10_extended_+_-_(37g) 0.43823 0.00000 0.00000
597 SOX10 SOX10_+_-_(37g) 0.43823 0.00000 0.00000
93 DLX5 DLX5_+_+_(36g) 0.43642 0.00000 0.00000
94 DLX5 DLX5_extended_+_+_(36g) 0.43642 0.00000 0.00000
651 SOX7 SOX7_+_-_(46g) 0.43409 0.00000 0.00000
10 ARID3A ARID3A_+_-_(90g) 0.42895 0.00000 0.00000
448 NR3C1 NR3C1_+_+_(264g) 0.42800 0.00000 0.00000
450 NR3C1 NR3C1_extended_+_+_(264g) 0.42800 0.00000 0.00000
133 ELF4 ELF4_extended_+_+_(45g) 0.42619 0.00000 0.00000
653 SOX7 SOX7_extended_+_-_(47g) 0.42523 0.00000 0.00000
44 BCLAF1 BCLAF1_+_-_(114g) 0.42424 0.00000 0.00000
46 BCLAF1 BCLAF1_extended_+_-_(114g) 0.42424 0.00000 0.00000
131 ELF4 ELF4_+_+_(42g) 0.42320 0.00000 0.00000
606 SOX13 SOX13_+_-_(19g) 0.42314 0.00000 0.00000
480 PKM PKM_+_-_(59g) 0.42207 0.00000 0.00000
482 PKM PKM_extended_+_-_(59g) 0.42207 0.00000 0.00000
208 FOXP1 FOXP1_extended_+_-_(113g) 0.41988 0.00000 0.00000
357 MEF2A MEF2A_extended_+_-_(258g) 0.41859 0.00000 0.00000
207 FOXP1 FOXP1_+_-_(122g) 0.41837 0.00000 0.00000
615 SOX1 SOX1_+_+_(10g) 0.41787 0.00000 0.00000
99 E2F3 E2F3_+_-_(39g) 0.41559 0.00000 0.00000
536 RB1 RB1_extended_+_-_(67g) 0.41445 0.00000 0.00000
534 RB1 RB1_+_-_(67g) 0.41445 0.00000 0.00000
586 SIX5 SIX5_+_+_(15g) 0.41154 0.00000 0.00000
588 SIX5 SIX5_extended_+_+_(15g) 0.41154 0.00000 0.00000
166 ETV5 ETV5_+_+_(198g) 0.40552 0.00000 0.00000
167 ETV5 ETV5_extended_+_+_(198g) 0.40552 0.00000 0.00000
47 BDP1 BDP1_+_-_(33g) 0.40526 0.00000 0.00000
355 MEF2A MEF2A_+_-_(233g) 0.40502 0.00000 0.00000
484 PLAGL1 PLAGL1_+_+_(40g) 0.40324 0.00000 0.00000
485 PLAGL1 PLAGL1_extended_+_+_(40g) 0.40324 0.00000 0.00000
682 SREBF1 SREBF1_+_+_(102g) 0.40308 0.00000 0.00000
683 SREBF1 SREBF1_extended_+_+_(102g) 0.40308 0.00000 0.00000
275 JDP2 JDP2_extended_+_-_(72g) 0.40234 0.00000 0.00000
824 ZNF467 ZNF467_extended_+_+_(21g) 0.40015 0.00000 0.00000
33 BACH1 BACH1_+_-_(79g) 0.39582 0.00000 0.00000
273 JDP2 JDP2_+_-_(74g) 0.39466 0.00000 0.00000
593 SMARCC1 SMARCC1_extended_+_+_(351g) 0.39405 0.00000 0.00000
592 SMARCC1 SMARCC1_+_+_(351g) 0.39405 0.00000 0.00000
250 HOXD1 HOXD1_extended_+_+_(9g) 0.38419 0.00000 0.00000
513 PRNP PRNP_extended_+_+_(21g) 0.38037 0.00000 0.00000
537 RBBP5 RBBP5_+_-_(52g) 0.37882 0.00000 0.00000
538 RBBP5 RBBP5_extended_+_-_(52g) 0.37882 0.00000 0.00000
156 ETS1 ETS1_extended_+_+_(155g) 0.37597 0.00000 0.00000
154 ETS1 ETS1_+_+_(153g) 0.37577 0.00000 0.00000
759 TPPP TPPP_+_-_(45g) 0.37268 0.00000 0.00000
761 TPPP TPPP_extended_+_-_(45g) 0.37268 0.00000 0.00000
684 SREBF2 SREBF2_+_-_(136g) 0.37245 0.00000 0.00000
685 SREBF2 SREBF2_extended_+_-_(136g) 0.37245 0.00000 0.00000
681 SPIB SPIB_extended_+_+_(78g) 0.37156 0.00000 0.00000
680 SPIB SPIB_+_+_(78g) 0.37156 0.00000 0.00000
585 SF1 SF1_extended_+_-_(15g) 0.37146 0.00000 0.00000
449 NR3C1 NR3C1_+_-_(108g) 0.37145 0.00000 0.00000
377 MSX1 MSX1_extended_+_-_(16g) 0.36819 0.00000 0.00000
100 E2F3 E2F3_extended_+_-_(50g) 0.36560 0.00000 0.00000
48 BDP1 BDP1_extended_+_-_(35g) 0.35797 0.00000 0.00000
773 YY1 YY1_+_+_(155g) 0.35785 0.00000 0.00000
701 STAT6 STAT6_+_+_(90g) 0.35706 0.00000 0.00000
702 STAT6 STAT6_extended_+_+_(90g) 0.35706 0.00000 0.00000
658 SOX8 SOX8_extended_+_-_(60g) 0.35624 0.00000 0.00000
375 MSX1 MSX1_+_-_(14g) 0.35621 0.00000 0.00000
153 ERG ERG_extended_+_-_(12g) 0.35490 0.00000 0.00000
655 SOX8 SOX8_+_-_(55g) 0.34885 0.00000 0.00000
11 ARID3A ARID3A_extended_+_-_(90g) 0.34422 0.00000 0.00000
587 SIX5 SIX5_+_-_(9g) 0.34351 0.00000 0.00000
589 SIX5 SIX5_extended_+_-_(9g) 0.34351 0.00000 0.00000
805 ZNF148 ZNF148_+_-_(73g) 0.34294 0.00000 0.00000
806 ZNF148 ZNF148_extended_+_-_(73g) 0.34294 0.00000 0.00000
527 RAD21 RAD21_+_-_(89g) 0.34052 0.00000 0.00000
528 RAD21 RAD21_extended_+_-_(89g) 0.34052 0.00000 0.00000
264 IRF2 IRF2_+_-_(143g) 0.33933 0.00000 0.00000
266 IRF2 IRF2_extended_+_-_(169g) 0.33566 0.00000 0.00000
108 EGR1 EGR1_extended_+_-_(27g) 0.33211 0.00000 0.00000
86 DBX2 DBX2_extended_+_+_(42g) 0.33207 0.00000 0.00000
435 NHLH2 NHLH2_+_+_(12g) 0.32727 0.00000 0.00000
53 CCDC25 CCDC25_+_-_(26g) 0.32682 0.00000 0.00000
54 CCDC25 CCDC25_extended_+_-_(26g) 0.32682 0.00000 0.00000
49 BRF2 BRF2_+_+_(35g) 0.32549 0.00000 0.00000
51 BRF2 BRF2_extended_+_+_(35g) 0.32549 0.00000 0.00000
318 KLF7 KLF7_extended_+_-_(27g) 0.32486 0.00000 0.00000
775 YY1 YY1_extended_+_+_(144g) 0.31961 0.00000 0.00000
55 CEBPB CEBPB_+_-_(57g) 0.31899 0.00000 0.00000
56 CEBPB CEBPB_extended_+_-_(58g) 0.31867 0.00000 0.00000
687 STAT1 STAT1_+_+_(180g) 0.31709 0.00000 0.00000
689 STAT1 STAT1_extended_+_+_(180g) 0.31709 0.00000 0.00000
80 CUX1 CUX1_+_-_(107g) 0.31404 0.00000 0.00000
83 CUX1 CUX1_extended_+_-_(107g) 0.31404 0.00000 0.00000
316 KLF7 KLF7_+_-_(20g) 0.30764 0.00000 0.00000
741 TEAD1 TEAD1_+_-_(31g) 0.30399 0.00000 0.00000
743 TEAD1 TEAD1_extended_+_-_(31g) 0.30399 0.00000 0.00000
629 SOX3 SOX3_extended_+_+_(11g) 0.30270 0.00000 0.00000
627 SOX3 SOX3_+_+_(11g) 0.30270 0.00000 0.00000
75 CTCF CTCF_+_-_(148g) 0.29659 0.00000 0.00000
652 SOX7 SOX7_extended_+_+_(7g) 0.29491 0.00000 0.00000
394 NF1 NF1_+_-_(354g) 0.29469 0.00000 0.00000
395 NF1 NF1_extended_+_-_(354g) 0.29469 0.00000 0.00000
785 ZBTB20 ZBTB20_extended_+_+_(33g) 0.29460 0.00000 0.00000
783 ZBTB20 ZBTB20_+_+_(33g) 0.29460 0.00000 0.00000
498 POU3F1 POU3F1_extended_+_+_(40g) 0.29036 0.00000 0.00000
496 POU3F1 POU3F1_+_+_(40g) 0.29036 0.00000 0.00000
351 MDM2 MDM2_+_+_(11g) 0.29020 0.00000 0.00000
352 MDM2 MDM2_extended_+_+_(11g) 0.29020 0.00000 0.00000
52 BRF2 BRF2_extended_+_-_(17g) 0.28911 0.00000 0.00000
50 BRF2 BRF2_+_-_(17g) 0.28911 0.00000 0.00000
694 STAT3 STAT3_extended_+_-_(118g) 0.28774 0.00000 0.00000
693 STAT3 STAT3_+_-_(118g) 0.28774 0.00000 0.00000
66 CIC CIC_extended_+_+_(12g) 0.28630 0.00000 0.00000
707 TAF7 TAF7_extended_+_+_(27g) 0.28496 0.00000 0.00000
705 TAF7 TAF7_+_+_(27g) 0.28496 0.00000 0.00000
165 ETV4 ETV4_extended_+_+_(42g) 0.27765 0.00000 0.00000
491 POLR3G POLR3G_extended_+_+_(21g) 0.27607 0.00000 0.00000
490 POLR3G POLR3G_+_+_(21g) 0.27607 0.00000 0.00000
535 RB1 RB1_extended_+_+_(30g) 0.27499 0.00000 0.00000
533 RB1 RB1_+_+_(30g) 0.27499 0.00000 0.00000
566 RFXANK RFXANK_+_-_(21g) 0.27473 0.00000 0.00000
567 RFXANK RFXANK_extended_+_-_(21g) 0.27473 0.00000 0.00000
366 MEF2D MEF2D_extended_+_+_(185g) 0.27472 0.00000 0.00000
373 MEOX2 MEOX2_extended_+_+_(38g) 0.27385 0.00000 0.00000
224 GPANK1 GPANK1_extended_+_-_(18g) 0.27359 0.00000 0.00000
222 GPANK1 GPANK1_+_-_(18g) 0.27359 0.00000 0.00000
231 HDAC2 HDAC2_+_+_(22g) 0.27310 0.00000 0.00000
233 HDAC2 HDAC2_extended_+_+_(22g) 0.27310 0.00000 0.00000
302 KLF16 KLF16_+_+_(11g) 0.27034 0.00000 0.00000
668 SP1 SP1_extended_+_+_(171g) 0.26329 0.00000 0.00000
372 MEOX2 MEOX2_+_+_(43g) 0.26053 0.00000 0.00000
138 ELK3 ELK3_extended_+_+_(57g) 0.25821 0.00000 0.00000
137 ELK3 ELK3_+_+_(56g) 0.25819 0.00000 0.00000
650 SOX7 SOX7_+_+_(5g) 0.25722 0.00000 0.00000
149 ERF ERF_+_+_(48g) 0.25494 0.00000 0.00000
150 ERF ERF_extended_+_+_(48g) 0.25494 0.00000 0.00000
453 NRF1 NRF1_+_-_(191g) 0.25493 0.00000 0.00000
581 RXRB RXRB_+_-_(10g) 0.25350 0.00000 0.00000
583 RXRB RXRB_extended_+_-_(10g) 0.25350 0.00000 0.00000
60 CHD2 CHD2_+_-_(38g) 0.24467 0.00000 0.00000
613 SOX15 SOX15_extended_+_-_(41g) 0.24385 0.00000 0.00000
610 SOX15 SOX15_+_-_(41g) 0.24385 0.00000 0.00000
37 BACH2 BACH2_+_-_(123g) 0.24160 0.00000 0.00000
637 SOX5 SOX5_+_+_(72g) 0.23907 0.00000 0.00000
541 RELB RELB_extended_+_+_(57g) 0.23885 0.00000 0.00000
819 ZNF3 ZNF3_extended_+_+_(12g) 0.23632 0.00000 0.00000
817 ZNF3 ZNF3_+_+_(12g) 0.23632 0.00000 0.00000
348 MAZ MAZ_extended_+_+_(195g) 0.23502 0.00000 0.00000
347 MAZ MAZ_+_+_(195g) 0.23502 0.00000 0.00000
788 ZBTB7A ZBTB7A_+_-_(46g) 0.23473 0.00000 0.00000
790 ZBTB7A ZBTB7A_extended_+_-_(46g) 0.23473 0.00000 0.00000
39 BACH2 BACH2_extended_+_-_(124g) 0.23418 0.00000 0.00000
77 CTCF CTCF_extended_+_-_(135g) 0.23296 0.00000 0.00000
745 TEF TEF_extended_+_+_(19g) 0.23066 0.00000 0.00000
630 SOX3 SOX3_extended_+_-_(9g) 0.23048 0.00000 0.00000
628 SOX3 SOX3_+_-_(9g) 0.23048 0.00000 0.00000
469 PAX7 PAX7_+_-_(13g) 0.22623 0.00000 0.00000
470 PAX7 PAX7_extended_+_-_(13g) 0.22623 0.00000 0.00000
454 NRF1 NRF1_extended_+_-_(217g) 0.22398 0.00000 0.00000
744 TEF TEF_+_+_(10g) 0.22370 0.00000 0.00000
365 MEF2D MEF2D_+_+_(175g) 0.22192 0.00000 0.00000
489 POLR2A POLR2A_extended_+_-_(154g) 0.22148 0.00000 0.00000
487 POLR2A POLR2A_+_-_(154g) 0.22148 0.00000 0.00000
771 YBX1 YBX1_+_-_(21g) 0.22105 0.00000 0.00000
772 YBX1 YBX1_extended_+_-_(21g) 0.22105 0.00000 0.00000
746 TFAP2C TFAP2C_+_-_(9g) 0.21832 0.00000 0.00000
747 TFAP2C TFAP2C_extended_+_-_(9g) 0.21832 0.00000 0.00000
379 MXI1 MXI1_+_-_(86g) 0.21665 0.00000 0.00000
380 MXI1 MXI1_extended_+_-_(86g) 0.21665 0.00000 0.00000
62 CHURC1 CHURC1_+_+_(143g) 0.21403 0.00000 0.00000
64 CHURC1 CHURC1_extended_+_+_(143g) 0.21403 0.00000 0.00000
456 ODC1 ODC1_extended_+_+_(55g) 0.21388 0.00000 0.00000
455 ODC1 ODC1_+_+_(55g) 0.21388 0.00000 0.00000
451 NR3C1 NR3C1_extended_+_-_(81g) 0.21109 0.00000 0.00000
164 ETV4 ETV4_+_+_(39g) 0.21064 0.00000 0.00000
666 SP1 SP1_+_+_(134g) 0.20614 0.00000 0.00000
304 KLF16 KLF16_extended_+_+_(15g) 0.20596 0.00000 0.00000
508 PRDM11 PRDM11_extended_+_-_(23g) 0.20559 0.00000 0.00000
87 DIABLO DIABLO_+_+_(23g) 0.20296 0.00000 0.00000
88 DIABLO DIABLO_extended_+_+_(23g) 0.20296 0.00000 0.00000
6 AGGF1 AGGF1_+_+_(136g) 0.20108 0.00000 0.00000
8 AGGF1 AGGF1_extended_+_+_(136g) 0.20108 0.00000 0.00000
97 E2F1 E2F1_+_-_(20g) 0.19795 0.00000 0.00000
643 SOX6 SOX6_+_+_(73g) 0.19677 0.00000 0.00000
323 KLF9 KLF9_extended_+_+_(23g) 0.19598 0.00000 0.00000
321 KLF9 KLF9_+_+_(23g) 0.19598 0.00000 0.00000
403 NFATC3 NFATC3_+_-_(54g) 0.19381 0.00000 0.00000
405 NFATC3 NFATC3_extended_+_-_(54g) 0.19381 0.00000 0.00000
386 NANOS1 NANOS1_+_+_(10g) 0.19269 0.00000 0.00000
387 NANOS1 NANOS1_extended_+_+_(10g) 0.19269 0.00000 0.00000
98 E2F1 E2F1_extended_+_-_(23g) 0.19228 0.00000 0.00000
317 KLF7 KLF7_extended_+_+_(29g) 0.19219 0.00000 0.00000
291 KLF11 KLF11_+_+_(19g) 0.19192 0.00000 0.00000
292 KLF11 KLF11_extended_+_+_(19g) 0.19192 0.00000 0.00000
239 HESX1 HESX1_+_-_(13g) 0.18838 0.00000 0.00000
240 HESX1 HESX1_extended_+_-_(13g) 0.18838 0.00000 0.00000
753 THRA THRA_extended_+_-_(50g) 0.18690 0.00000 0.00000
751 THRA THRA_+_-_(50g) 0.18690 0.00000 0.00000
497 POU3F1 POU3F1_+_-_(15g) 0.18447 0.00000 0.00000
499 POU3F1 POU3F1_extended_+_-_(15g) 0.18447 0.00000 0.00000
105 E2F6 E2F6_extended_+_-_(14g) 0.18308 0.00000 0.00000
104 E2F6 E2F6_+_-_(14g) 0.18308 0.00000 0.00000
3 ACO1 ACO1_extended_+_-_(29g) 0.18298 0.00000 0.00000
1 ACO1 ACO1_+_-_(29g) 0.18298 0.00000 0.00000
647 SOX6 SOX6_extended_+_+_(83g) 0.18191 0.00000 0.00000
109 EGR2 EGR2_+_+_(27g) 0.18062 0.00000 0.00000
488 POLR2A POLR2A_extended_+_+_(188g) 0.17979 0.00000 0.00000
486 POLR2A POLR2A_+_+_(188g) 0.17979 0.00000 0.00000
791 ZBTB7B ZBTB7B_+_+_(31g) 0.17942 0.00000 0.00000
792 ZBTB7B ZBTB7B_extended_+_+_(31g) 0.17942 0.00000 0.00000
110 EGR2 EGR2_extended_+_+_(129g) 0.17878 0.00000 0.00000
59 CHD2 CHD2_+_+_(43g) 0.17273 0.00000 0.00000
61 CHD2 CHD2_extended_+_+_(45g) 0.17248 0.00000 0.00000
324 KLF9 KLF9_extended_+_-_(21g) 0.16874 0.00000 0.00000
322 KLF9 KLF9_+_-_(21g) 0.16874 0.00000 0.00000
335 LUZP2 LUZP2_+_-_(9g) 0.16854 0.00000 0.00000
336 LUZP2 LUZP2_extended_+_-_(9g) 0.16854 0.00000 0.00000
471 PBX1 PBX1_extended_+_-_(30g) 0.16735 0.00000 0.00000
778 ZBTB11 ZBTB11_extended_+_+_(45g) 0.15496 0.00000 0.00000
777 ZBTB11 ZBTB11_+_+_(45g) 0.15496 0.00000 0.00000
512 PRNP PRNP_+_-_(57g) 0.14748 0.00000 0.00000
514 PRNP PRNP_extended_+_-_(57g) 0.14748 0.00000 0.00000
667 SP1 SP1_+_-_(127g) 0.14684 0.00000 0.00000
810 ZNF281 ZNF281_extended_+_-_(59g) 0.14412 0.00000 0.00000
809 ZNF281 ZNF281_+_-_(59g) 0.14412 0.00000 0.00000
261 IRF1 IRF1_+_+_(13g) 0.14364 0.00000 0.00000
262 IRF1 IRF1_extended_+_+_(13g) 0.14364 0.00000 0.00000
258 IKZF2 IKZF2_extended_+_+_(20g) 0.13538 0.00000 0.00000
257 IKZF2 IKZF2_+_+_(20g) 0.13538 0.00000 0.00000
501 POU3F3 POU3F3_extended_+_+_(16g) 0.13220 0.00000 0.00000
309 KLF3 KLF3_extended_+_+_(87g) 0.13051 0.00000 0.00000
308 KLF3 KLF3_+_+_(86g) 0.12991 0.00000 0.00000
749 TFAP4 TFAP4_extended_+_+_(12g) 0.12834 0.00000 0.00000
748 TFAP4 TFAP4_+_+_(12g) 0.12834 0.00000 0.00000
500 POU3F3 POU3F3_+_+_(13g) 0.12520 0.00000 0.00000
191 FOXK1 FOXK1_extended_+_+_(56g) 0.12050 0.00000 0.00000
504 POU3F4 POU3F4_extended_+_-_(19g) 0.11885 0.00000 0.00000
763 TRIM28 TRIM28_extended_+_-_(43g) 0.11703 0.00000 0.00000
762 TRIM28 TRIM28_+_-_(43g) 0.11703 0.00000 0.00000
669 SP1 SP1_extended_+_-_(134g) 0.11635 0.00000 0.00000
134 ELF4 ELF4_extended_+_-_(34g) 0.11271 0.00001 0.00001
802 ZNF134 ZNF134_extended_+_-_(24g) 0.10510 0.00003 0.00003
800 ZNF134 ZNF134_+_-_(24g) 0.10510 0.00003 0.00003
776 YY1 YY1_extended_+_-_(89g) 0.10488 0.00003 0.00003
774 YY1 YY1_+_-_(92g) 0.10408 0.00003 0.00004
284 JUND JUND_extended_+_-_(69g) 0.10234 0.00004 0.00005
358 MEF2B MEF2B_+_+_(32g) 0.09835 0.00008 0.00010
688 STAT1 STAT1_+_-_(16g) 0.09013 0.00031 0.00036
690 STAT1 STAT1_extended_+_-_(16g) 0.09013 0.00031 0.00036
193 FOXK2 FOXK2_extended_+_-_(146g) 0.08913 0.00036 0.00042
442 NR2C2 NR2C2_+_+_(77g) 0.08909 0.00036 0.00042
444 NR2C2 NR2C2_extended_+_+_(77g) 0.08909 0.00036 0.00042
315 KLF7 KLF7_+_+_(10g) 0.08863 0.00039 0.00045
102 E2F4 E2F4_extended_+_+_(56g) 0.08683 0.00051 0.00059
139 ELK4 ELK4_+_+_(194g) 0.08477 0.00069 0.00079
140 ELK4 ELK4_extended_+_+_(194g) 0.08477 0.00069 0.00079
699 STAT5A STAT5A_extended_+_+_(9g) 0.08343 0.00084 0.00095
697 STAT5A STAT5A_+_+_(9g) 0.08343 0.00084 0.00095
700 STAT5A STAT5A_extended_+_-_(12g) 0.08339 0.00084 0.00096
698 STAT5A STAT5A_+_-_(12g) 0.08339 0.00084 0.00096
132 ELF4 ELF4_+_-_(20g) 0.07817 0.00175 0.00199
603 SOX12 SOX12_+_-_(21g) 0.07762 0.00189 0.00213
821 ZNF444 ZNF444_+_-_(11g) 0.07647 0.00221 0.00248
822 ZNF444 ZNF444_extended_+_-_(11g) 0.07647 0.00221 0.00248
367 MEF2D MEF2D_extended_+_-_(20g) 0.07553 0.00250 0.00281
268 IRF3 IRF3_extended_+_+_(22g) 0.07538 0.00255 0.00286
281 JUND JUND_+_-_(38g) 0.07013 0.00501 0.00558
0 ACO1 ACO1_+_+_(23g) 0.06572 0.00855 0.00948
2 ACO1 ACO1_extended_+_+_(23g) 0.06572 0.00855 0.00948
686 SRF SRF_extended_+_-_(14g) 0.06265 0.01220 0.01351
115 EHF EHF_extended_+_+_(16g) 0.06080 0.01499 0.01657
113 EHF EHF_+_+_(16g) 0.06080 0.01499 0.01657
219 GABPB1 GABPB1_extended_+_+_(99g) 0.05986 0.01664 0.01834
217 GABPB1 GABPB1_+_+_(99g) 0.05986 0.01664 0.01834
103 E2F4 E2F4_extended_+_-_(41g) 0.05951 0.01729 0.01903
703 TAF1 TAF1_+_-_(56g) 0.05855 0.01918 0.02105
704 TAF1 TAF1_extended_+_-_(56g) 0.05855 0.01918 0.02105
267 IRF3 IRF3_+_+_(10g) 0.05823 0.01984 0.02175
116 EHF EHF_extended_+_-_(13g) 0.05788 0.02060 0.02252
114 EHF EHF_+_-_(13g) 0.05788 0.02060 0.02252
101 E2F4 E2F4_+_-_(15g) 0.05465 0.02881 0.03126
303 KLF16 KLF16_+_-_(12g) 0.03726 0.13627 0.14573
305 KLF16 KLF16_extended_+_-_(13g) 0.03648 0.14472 0.15457
341 MAFG MAFG_+_-_(27g) 0.03643 0.14522 0.15471
342 MAFG MAFG_extended_+_-_(27g) 0.03643 0.14522 0.15471
463 PAX5 PAX5_+_+_(16g) 0.02960 0.23668 0.24989
464 PAX5 PAX5_extended_+_+_(16g) 0.02960 0.23668 0.24989
511 PRNP PRNP_+_+_(10g) 0.02942 0.23958 0.25231
252 HSF2 HSF2_extended_+_+_(10g) 0.02809 0.26142 0.27497
780 ZBTB14 ZBTB14_extended_+_-_(21g) 0.02721 0.27679 0.29040
779 ZBTB14 ZBTB14_+_-_(21g) 0.02721 0.27679 0.29040
271 IRF9 IRF9_+_+_(21g) 0.02512 0.31529 0.32872
248 HOMEZ HOMEZ_extended_+_-_(16g) 0.01939 0.43834 0.45080
247 HOMEZ HOMEZ_+_-_(16g) 0.01939 0.43834 0.45080
724 TCF3 TCF3_extended_+_-_(60g) 0.01825 0.46560 0.47824
711 TBP TBP_+_+_(66g) 0.01581 0.52751 0.53917
712 TBP TBP_extended_+_+_(66g) 0.01581 0.52751 0.53917
445 NR2C2 NR2C2_extended_+_-_(69g) 0.01068 0.66941 0.68253
443 NR2C2 NR2C2_+_-_(69g) 0.01068 0.66941 0.68253
799 ZNF134 ZNF134_+_+_(65g) 0.00791 0.75201 0.76302
801 ZNF134 ZNF134_extended_+_+_(65g) 0.00791 0.75201 0.76302
221 GPANK1 GPANK1_+_+_(13g) 0.00646 0.79630 0.80501
223 GPANK1 GPANK1_extended_+_+_(13g) 0.00646 0.79630 0.80501
582 RXRB RXRB_extended_+_+_(18g) 0.00418 0.86737 0.87473
580 RXRB RXRB_+_+_(18g) 0.00418 0.86737 0.87473
722 TCF3 TCF3_+_-_(51g) 0.00333 0.89415 0.90065
723 TCF3 TCF3_extended_+_+_(43g) 0.00238 0.92432 0.92990
229 HDAC1 HDAC1_+_-_(19g) 0.00018 0.99425 0.99425
230 HDAC1 HDAC1_extended_+_-_(19g) 0.00018 0.99425 0.99425
803 ZNF143 ZNF143_+_+_(66g) -0.00038 0.98787 0.99025
804 ZNF143 ZNF143_extended_+_+_(66g) -0.00038 0.98787 0.99025
721 TCF3 TCF3_+_+_(40g) -0.00064 0.97974 0.98448
272 IRF9 IRF9_extended_+_+_(26g) -0.00712 0.77607 0.78647
540 RELA RELA_extended_+_-_(18g) -0.00847 0.73495 0.74753
752 THRA THRA_extended_+_+_(79g) -0.00944 0.70591 0.71887
436 NKX3-1 NKX3-1_+_+_(12g) -0.01702 0.49628 0.50851
437 NKX3-1 NKX3-1_extended_+_+_(12g) -0.01702 0.49628 0.50851
529 RARA RARA_+_+_(16g) -0.02082 0.40518 0.41773
530 RARA RARA_extended_+_+_(16g) -0.02082 0.40518 0.41773
67 CPSF4 CPSF4_+_-_(11g) -0.02288 0.36045 0.37254
69 CPSF4 CPSF4_extended_+_-_(11g) -0.02288 0.36045 0.37254
385 MZF1 MZF1_extended_+_+_(78g) -0.02348 0.34786 0.36042
384 MZF1 MZF1_+_+_(78g) -0.02348 0.34786 0.36042
404 NFATC3 NFATC3_extended_+_+_(33g) -0.02367 0.34413 0.35745
402 NFATC3 NFATC3_+_+_(33g) -0.02367 0.34413 0.35745
76 CTCF CTCF_extended_+_+_(160g) -0.02436 0.33010 0.34373
820 ZNF3 ZNF3_extended_+_-_(16g) -0.02587 0.30113 0.31436
818 ZNF3 ZNF3_+_-_(16g) -0.02587 0.30113 0.31436
346 MAGEF1 MAGEF1_extended_+_-_(16g) -0.02649 0.28971 0.30319
345 MAGEF1 MAGEF1_+_-_(16g) -0.02649 0.28971 0.30319
564 RFX5 RFX5_extended_+_+_(17g) -0.02945 0.23913 0.25217
731 TCF4 TCF4_extended_-_+_(14g) -0.02960 0.23665 0.24989
569 RFXAP RFXAP_extended_+_-_(35g) -0.03031 0.22563 0.23914
568 RFXAP RFXAP_+_-_(35g) -0.03031 0.22563 0.23914
727 TCF4 TCF4_-_+_(13g) -0.03125 0.21156 0.22479
563 RFX5 RFX5_+_+_(51g) -0.03477 0.16443 0.17494
204 FOXO3 FOXO3_+_-_(149g) -0.04070 0.10363 0.11096
604 SOX12 SOX12_extended_+_-_(79g) -0.04169 0.09551 0.10240
206 FOXO3 FOXO3_extended_+_-_(150g) -0.04221 0.09148 0.09820
811 ZNF341 ZNF341_+_+_(36g) -0.04801 0.05484 0.05895
813 ZNF341 ZNF341_extended_+_+_(36g) -0.04801 0.05484 0.05895
27 ATF1 ATF1_+_+_(21g) -0.04975 0.04662 0.05024
45 BCLAF1 BCLAF1_extended_+_+_(115g) -0.05317 0.03344 0.03608
43 BCLAF1 BCLAF1_+_+_(115g) -0.05317 0.03344 0.03608
570 RIOK2 RIOK2_+_+_(20g) -0.05405 0.03061 0.03312
572 RIOK2 RIOK2_extended_+_+_(20g) -0.05405 0.03061 0.03312
192 FOXK2 FOXK2_extended_+_+_(45g) -0.05484 0.02827 0.03070
706 TAF7 TAF7_+_-_(40g) -0.05600 0.02508 0.02728
708 TAF7 TAF7_extended_+_-_(40g) -0.05600 0.02508 0.02728
354 MECP2 MECP2_extended_+_+_(21g) -0.05748 0.02150 0.02344
353 MECP2 MECP2_+_+_(21g) -0.05748 0.02150 0.02344
265 IRF2 IRF2_extended_+_+_(96g) -0.06838 0.00621 0.00691
218 GABPB1 GABPB1_+_-_(17g) -0.07239 0.00377 0.00420
220 GABPB1 GABPB1_extended_+_-_(17g) -0.07239 0.00377 0.00420
675 SP4 SP4_extended_+_-_(124g) -0.07400 0.00306 0.00342
673 SP3 SP3_extended_+_+_(349g) -0.07445 0.00289 0.00323
215 GABPA GABPA_extended_+_+_(105g) -0.07684 0.00210 0.00237
214 GABPA GABPA_+_+_(101g) -0.07793 0.00181 0.00205
474 PBX4 PBX4_extended_+_-_(11g) -0.08178 0.00106 0.00120
136 ELK1 ELK1_extended_+_+_(70g) -0.08267 0.00093 0.00106
135 ELK1 ELK1_+_+_(70g) -0.08267 0.00093 0.00106
320 KLF8 KLF8_extended_+_+_(11g) -0.08402 0.00077 0.00088
830 ZNF579 ZNF579_extended_+_+_(20g) -0.08411 0.00076 0.00087
829 ZNF579 ZNF579_+_+_(20g) -0.08411 0.00076 0.00087
674 SP4 SP4_+_-_(129g) -0.08612 0.00056 0.00065
476 PHF8 PHF8_extended_+_+_(29g) -0.08631 0.00055 0.00063
475 PHF8 PHF8_+_+_(29g) -0.08631 0.00055 0.00063
319 KLF8 KLF8_+_+_(10g) -0.08891 0.00037 0.00043
189 FOXJ2 FOXJ2_+_+_(22g) -0.09763 0.00009 0.00011
672 SP3 SP3_+_+_(335g) -0.09796 0.00009 0.00010
190 FOXJ2 FOXJ2_extended_+_+_(24g) -0.09833 0.00008 0.00010
539 RELA RELA_extended_+_+_(14g) -0.09937 0.00007 0.00008
263 IRF2 IRF2_+_+_(75g) -0.10292 0.00004 0.00004
216 GABPA GABPA_extended_+_-_(133g) -0.10680 0.00002 0.00002
692 STAT2 STAT2_extended_+_+_(22g) -0.10943 0.00001 0.00001
691 STAT2 STAT2_+_+_(22g) -0.10943 0.00001 0.00001
816 ZNF354A ZNF354A_extended_+_-_(10g) -0.11273 0.00001 0.00001
815 ZNF354A ZNF354A_+_-_(10g) -0.11273 0.00001 0.00001
213 FOXP4 FOXP4_extended_+_-_(14g) -0.11343 0.00001 0.00001
671 SP2 SP2_extended_+_+_(120g) -0.11364 0.00001 0.00001
670 SP2 SP2_+_+_(81g) -0.12044 0.00000 0.00000
619 SOX21 SOX21_extended_-_+_(4g) -0.13010 0.00000 0.00000
235 HDX HDX_+_+_(52g) -0.13094 0.00000 0.00000
236 HDX HDX_extended_+_+_(52g) -0.13094 0.00000 0.00000
796 ZEB2 ZEB2_-_+_(11g) -0.14769 0.00000 0.00000
798 ZEB2 ZEB2_extended_-_+_(11g) -0.14769 0.00000 0.00000
246 HNF4G HNF4G_extended_-_+_(7g) -0.14786 0.00000 0.00000
244 HNF4G HNF4G_-_+_(7g) -0.14786 0.00000 0.00000
74 CTCF CTCF_+_+_(125g) -0.15006 0.00000 0.00000
68 CPSF4 CPSF4_extended_+_+_(24g) -0.15028 0.00000 0.00000
85 DBP DBP_+_+_(10g) -0.15453 0.00000 0.00000
573 RIOK2 RIOK2_extended_+_-_(22g) -0.16081 0.00000 0.00000
571 RIOK2 RIOK2_+_-_(22g) -0.16081 0.00000 0.00000
807 ZNF263 ZNF263_+_-_(14g) -0.17445 0.00000 0.00000
808 ZNF263 ZNF263_extended_+_-_(14g) -0.17445 0.00000 0.00000
612 SOX15 SOX15_extended_+_+_(10g) -0.18080 0.00000 0.00000
609 SOX15 SOX15_+_+_(10g) -0.18080 0.00000 0.00000
286 JUND JUND_extended_-_-_(17g) -0.18124 0.00000 0.00000
283 JUND JUND_-_-_(17g) -0.18124 0.00000 0.00000
285 JUND JUND_extended_-_+_(11g) -0.19399 0.00000 0.00000
282 JUND JUND_-_+_(9g) -0.19504 0.00000 0.00000
481 PKM PKM_extended_+_+_(24g) -0.20123 0.00000 0.00000
479 PKM PKM_+_+_(24g) -0.20123 0.00000 0.00000
28 ATF1 ATF1_extended_+_+_(28g) -0.20515 0.00000 0.00000
71 CREB1 CREB1_extended_+_+_(40g) -0.21076 0.00000 0.00000
70 CREB1 CREB1_+_+_(40g) -0.21076 0.00000 0.00000
7 AGGF1 AGGF1_+_-_(33g) -0.21304 0.00000 0.00000
9 AGGF1 AGGF1_extended_+_-_(33g) -0.21304 0.00000 0.00000
433 NFYC NFYC_+_-_(15g) -0.21636 0.00000 0.00000
483 PKNOX1 PKNOX1_extended_+_-_(11g) -0.21916 0.00000 0.00000
205 FOXO3 FOXO3_extended_+_+_(52g) -0.22874 0.00000 0.00000
17 ARNT ARNT_extended_+_-_(10g) -0.22931 0.00000 0.00000
16 ARNT ARNT_+_-_(10g) -0.22931 0.00000 0.00000
203 FOXO3 FOXO3_+_+_(51g) -0.23182 0.00000 0.00000
23 ASCL1 ASCL1_-_+_(10g) -0.23300 0.00000 0.00000
65 CHURC1 CHURC1_extended_+_-_(10g) -0.24091 0.00000 0.00000
63 CHURC1 CHURC1_+_-_(10g) -0.24091 0.00000 0.00000
477 PICK1 PICK1_+_+_(23g) -0.24619 0.00000 0.00000
478 PICK1 PICK1_extended_+_+_(23g) -0.24619 0.00000 0.00000
288 JUN JUN_-_-_(5g) -0.25056 0.00000 0.00000
290 JUN JUN_extended_-_-_(5g) -0.25056 0.00000 0.00000
614 SOX15 SOX15_extended_-_+_(10g) -0.25354 0.00000 0.00000
611 SOX15 SOX15_-_+_(10g) -0.25354 0.00000 0.00000
370 MEIS2 MEIS2_extended_-_+_(7g) -0.25401 0.00000 0.00000
814 ZNF341 ZNF341_extended_+_-_(10g) -0.26374 0.00000 0.00000
812 ZNF341 ZNF341_+_-_(10g) -0.26374 0.00000 0.00000
434 NFYC NFYC_extended_+_-_(15g) -0.27620 0.00000 0.00000
633 SOX4 SOX4_-_+_(19g) -0.28562 0.00000 0.00000
259 ING4 ING4_+_+_(12g) -0.29175 0.00000 0.00000
260 ING4 ING4_extended_+_+_(12g) -0.29175 0.00000 0.00000
378 MTF1 MTF1_extended_+_+_(51g) -0.29353 0.00000 0.00000
226 GTF2I GTF2I_extended_+_+_(25g) -0.30054 0.00000 0.00000
225 GTF2I GTF2I_+_+_(25g) -0.30054 0.00000 0.00000
636 SOX4 SOX4_extended_-_+_(21g) -0.30060 0.00000 0.00000
129 ELF2 ELF2_extended_-_+_(29g) -0.31150 0.00000 0.00000
126 ELF2 ELF2_-_+_(27g) -0.31171 0.00000 0.00000
766 VEZF1 VEZF1_-_+_(24g) -0.32206 0.00000 0.00000
768 VEZF1 VEZF1_extended_-_+_(24g) -0.32206 0.00000 0.00000
119 ELF1 ELF1_-_+_(23g) -0.32516 0.00000 0.00000
123 ELF1 ELF1_extended_-_+_(23g) -0.32516 0.00000 0.00000
26 ASCL1 ASCL1_extended_-_+_(16g) -0.32663 0.00000 0.00000
732 TCF4 TCF4_extended_-_-_(12g) -0.32747 0.00000 0.00000
728 TCF4 TCF4_-_-_(9g) -0.32967 0.00000 0.00000
127 ELF2 ELF2_-_-_(11g) -0.34495 0.00000 0.00000
195 FOXN2 FOXN2_extended_-_+_(14g) -0.35486 0.00000 0.00000
130 ELF2 ELF2_extended_-_-_(14g) -0.35579 0.00000 0.00000
505 POU6F1 POU6F1_extended_+_+_(41g) -0.35826 0.00000 0.00000
556 RFX3 RFX3_extended_-_-_(7g) -0.37059 0.00000 0.00000
554 RFX3 RFX3_-_-_(7g) -0.37059 0.00000 0.00000
750 THRA THRA_+_+_(44g) -0.37346 0.00000 0.00000
591 SMAD1 SMAD1_extended_-_+_(10g) -0.38317 0.00000 0.00000
590 SMAD1 SMAD1_-_+_(10g) -0.38317 0.00000 0.00000
84 CUX1 CUX1_extended_-_-_(10g) -0.38487 0.00000 0.00000
81 CUX1 CUX1_-_-_(10g) -0.38487 0.00000 0.00000
665 SOX9 SOX9_extended_-_-_(11g) -0.38496 0.00000 0.00000
662 SOX9 SOX9_-_-_(11g) -0.38496 0.00000 0.00000
737 TCF7L2 TCF7L2_-_-_(11g) -0.40316 0.00000 0.00000
621 SOX2 SOX2_-_+_(16g) -0.41185 0.00000 0.00000
625 SOX2 SOX2_extended_-_+_(16g) -0.41185 0.00000 0.00000
550 RFX2 RFX2_-_-_(17g) -0.42644 0.00000 0.00000
552 RFX2 RFX2_extended_-_-_(17g) -0.42644 0.00000 0.00000
276 JDP2 JDP2_extended_-_-_(11g) -0.44133 0.00000 0.00000
274 JDP2 JDP2_-_-_(11g) -0.44133 0.00000 0.00000
559 RFX4 RFX4_-_-_(44g) -0.44747 0.00000 0.00000
78 CTCF CTCF_extended_-_-_(7g) -0.45121 0.00000 0.00000
649 SOX6 SOX6_extended_-_+_(157g) -0.45203 0.00000 0.00000
645 SOX6 SOX6_-_+_(144g) -0.45409 0.00000 0.00000
646 SOX6 SOX6_-_-_(8g) -0.45909 0.00000 0.00000
401 NFAT5 NFAT5_extended_-_-_(15g) -0.46115 0.00000 0.00000
398 NFAT5 NFAT5_-_-_(15g) -0.46115 0.00000 0.00000
494 POU2F1 POU2F1_extended_-_-_(11g) -0.46872 0.00000 0.00000
524 PURA PURA_-_-_(7g) -0.46881 0.00000 0.00000
526 PURA PURA_extended_-_-_(7g) -0.46881 0.00000 0.00000
562 RFX4 RFX4_extended_-_-_(44g) -0.47094 0.00000 0.00000
426 NFIC NFIC_extended_-_-_(15g) -0.48520 0.00000 0.00000
424 NFIC NFIC_-_-_(15g) -0.48520 0.00000 0.00000
146 EMX2 EMX2_extended_-_+_(10g) -0.48590 0.00000 0.00000
622 SOX2 SOX2_-_-_(29g) -0.48735 0.00000 0.00000
659 SOX8 SOX8_extended_-_-_(43g) -0.49344 0.00000 0.00000
716 TCF12 TCF12_-_-_(159g) -0.49352 0.00000 0.00000
656 SOX8 SOX8_-_-_(40g) -0.49474 0.00000 0.00000
720 TCF12 TCF12_extended_-_-_(162g) -0.49511 0.00000 0.00000
626 SOX2 SOX2_extended_-_-_(31g) -0.49539 0.00000 0.00000
468 PAX6 PAX6_extended_-_-_(7g) -0.50860 0.00000 0.00000
784 ZBTB20 ZBTB20_-_-_(12g) -0.53503 0.00000 0.00000
786 ZBTB20 ZBTB20_extended_-_-_(12g) -0.53503 0.00000 0.00000
520 PRRX1 PRRX1_extended_-_-_(50g) -0.53665 0.00000 0.00000
517 PRRX1 PRRX1_-_-_(47g) -0.54149 0.00000 0.00000
356 MEF2A MEF2A_-_-_(13g) -0.54730 0.00000 0.00000
188 FOXG1 FOXG1_extended_-_+_(25g) -0.54948 0.00000 0.00000
383 MYEF2 MYEF2_extended_-_-_(8g) -0.55894 0.00000 0.00000
34 BACH1 BACH1_-_+_(11g) -0.55989 0.00000 0.00000
40 BACH2 BACH2_extended_-_-_(13g) -0.58589 0.00000 0.00000
38 BACH2 BACH2_-_-_(12g) -0.58946 0.00000 0.00000
412 NFE2L2 NFE2L2_extended_-_+_(21g) -0.60579 0.00000 0.00000
410 NFE2L2 NFE2L2_-_+_(21g) -0.60579 0.00000 0.00000
120 ELF1 ELF1_-_-_(33g) -0.60647 0.00000 0.00000
124 ELF1 ELF1_extended_-_-_(33g) -0.60647 0.00000 0.00000
36 BACH1 BACH1_extended_-_+_(13g) -0.60754 0.00000 0.00000
719 TCF12 TCF12_extended_-_+_(235g) -0.61310 0.00000 0.00000
548 RFX1 RFX1_extended_-_-_(7g) -0.61311 0.00000 0.00000
546 RFX1 RFX1_-_-_(6g) -0.61920 0.00000 0.00000
184 FOS FOS_extended_-_+_(52g) -0.62457 0.00000 0.00000
182 FOS FOS_-_+_(52g) -0.62457 0.00000 0.00000
280 JUNB JUNB_extended_-_+_(16g) -0.63040 0.00000 0.00000
278 JUNB JUNB_-_+_(16g) -0.63040 0.00000 0.00000
212 FOXP2 FOXP2_extended_-_+_(10g) -0.65009 0.00000 0.00000
561 RFX4 RFX4_extended_-_+_(54g) -0.65041 0.00000 0.00000
202 FOXO1 FOXO1_extended_-_-_(47g) -0.65295 0.00000 0.00000
199 FOXO1 FOXO1_-_-_(47g) -0.65295 0.00000 0.00000
642 SOX5 SOX5_extended_-_+_(249g) -0.65540 0.00000 0.00000
715 TCF12 TCF12_-_+_(161g) -0.65618 0.00000 0.00000
639 SOX5 SOX5_-_+_(225g) -0.65626 0.00000 0.00000
558 RFX4 RFX4_-_+_(49g) -0.65850 0.00000 0.00000
210 FOXP2 FOXP2_-_+_(9g) -0.65910 0.00000 0.00000
371 MEIS2 MEIS2_extended_-_-_(15g) -0.67686 0.00000 0.00000
343 MAF MAF_-_-_(14g) -0.67815 0.00000 0.00000
344 MAF MAF_extended_-_-_(12g) -0.68341 0.00000 0.00000
173 FLI1 FLI1_-_-_(9g) -0.68762 0.00000 0.00000
432 NFIX NFIX_extended_-_-_(12g) -0.68981 0.00000 0.00000
429 NFIX NFIX_-_-_(12g) -0.68981 0.00000 0.00000
294 KLF12 KLF12_-_+_(57g) -0.72597 0.00000 0.00000
298 KLF12 KLF12_extended_-_+_(57g) -0.72597 0.00000 0.00000
364 MEF2C MEF2C_extended_-_-_(106g) -0.73176 0.00000 0.00000
361 MEF2C MEF2C_-_-_(112g) -0.73376 0.00000 0.00000
295 KLF12 KLF12_-_-_(16g) -0.74768 0.00000 0.00000
299 KLF12 KLF12_extended_-_-_(16g) -0.74768 0.00000 0.00000
420 NFIA NFIA_extended_-_-_(71g) -0.76011 0.00000 0.00000
416 NFIA NFIA_-_-_(71g) -0.76011 0.00000 0.00000
419 NFIA NFIA_extended_-_+_(72g) -0.78629 0.00000 0.00000
415 NFIA NFIA_-_+_(72g) -0.78629 0.00000 0.00000

We can additionally plot eRegulon enrichment versus TF expression for each pseudobulk.

[11]:
# Region based
%matplotlib inline
import seaborn as sns
sns.set_style("white")
colors = ["#E9842C","#F8766D", "#BC9D00", "#00C0B4", "#9CA700", "#6FB000", "#00B813", "#00BD61", "#00C08E", "#00BDD4",
           "#00A7FF", "#7F96FF", "#E26EF7", "#FF62BF", "#D69100", "#BC81FF"]
categories = sorted(set(scplus_obj.metadata_cell['ACC_Seurat_cell_type']))
color_dict = dict(zip(categories, colors[0:len(categories)]))
prune_plot(scplus_obj,
           'SOX5_-_+',
           pseudobulk_variable = 'ACC_Seurat_cell_type',
           show_dot_plot = True,
           show_line_plot = False,
           color_dict = color_dict,
           use_pseudobulk = True,
           auc_key = 'eRegulon_AUC',
           signature_key = 'Region_based',
           seed=555)
_images/single_sample_tutorial_97_0.png
[12]:
# Gene based
%matplotlib inline
sns.set_style("white")
colors = ["#E9842C","#F8766D", "#BC9D00", "#00C0B4", "#9CA700", "#6FB000", "#00B813", "#00BD61", "#00C08E", "#00BDD4",
           "#00A7FF", "#7F96FF", "#E26EF7", "#FF62BF", "#D69100", "#BC81FF"]
categories = sorted(set(scplus_obj.metadata_cell['ACC_Seurat_cell_type']))
color_dict = dict(zip(categories, colors[0:len(categories)]))
prune_plot(scplus_obj,
           'SOX5_-_+',
           pseudobulk_variable = 'ACC_Seurat_cell_type',
           show_dot_plot = True,
           show_line_plot = False,
           color_dict = color_dict,
           use_pseudobulk = True,
           auc_key = 'eRegulon_AUC',
           signature_key = 'Gene_based',
           seed=555)
_images/single_sample_tutorial_98_0.png

We will select a subset of cistromes with good correlation with the TF. We will only use the extended eRegulon if there is not a direct eRegulon available.

[3]:
# Direct cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_gene_based']
selected_cistromes_1 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.5)]
selected_cistromes_2 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.5)]
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_region_based']
selected_cistromes_3 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.5)]
selected_cistromes_4 = df_corr[(~df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.5)]
selected_cistromes_direct = pd.concat([selected_cistromes_1, selected_cistromes_2, selected_cistromes_3, selected_cistromes_4])
# Extended cistromes
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_gene_based']
selected_cistromes_1 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.6)]
selected_cistromes_2 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.6)]
df_corr = scplus_obj.uns['TF_cistrome_correlation']['ACC_Seurat_cell_type_eGRN_region_based']
selected_cistromes_3 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] > 0.6)]
selected_cistromes_4 = df_corr[(df_corr['Cistrome'].str.contains('_extended')) & (df_corr['Rho'] < -0.6)]
selected_cistromes_extended = pd.concat([selected_cistromes_1, selected_cistromes_2, selected_cistromes_3, selected_cistromes_4])
# Only use extended if direct not available
selected_cistromes_extended = selected_cistromes_extended[~selected_cistromes_extended['TF'].isin(selected_cistromes_direct['TF'])]
# Combine
selected_cistromes = pd.concat([selected_cistromes_direct, selected_cistromes_extended]).drop_duplicates()

To assess the relationship between regions and genes in the same eRegulon, we can generate a dotplot per cell type.

[34]:
## Format data
from scenicplus.plotting.dotplot import *
# Gene based
dotplot_df_gene = generate_dotplot_df_AUC(scplus_obj,
                       'eRegulon_AUC',
                       'Gene_based',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_cistromes = selected_cistromes['Cistrome'],
                       use_pseudobulk = False,
                       normalize_expression = False,
                       standardize_expression = True,
                       standardize_auc = True)
dotplot_df_gene['is_extended'] = ['_extended' in x for x in dotplot_df_gene['Name']]
dotplot_df_gene['Consensus_name'] = [re.sub('_\(.*\)', '', cistrome_name) for cistrome_name in dotplot_df_gene['Name']]
# Region based
dotplot_df_region = generate_dotplot_df_AUC(scplus_obj,
                       'eRegulon_AUC',
                       'Region_based',
                       'ACC_Seurat_cell_type',
                       subset = None,
                       subset_cistromes = selected_cistromes['Cistrome'],
                       use_pseudobulk = False,
                       normalize_expression = False,
                       standardize_expression = True,
                       standardize_auc = True)
dotplot_df_region['is_extended'] = ['_extended' in x for x in dotplot_df_region['Name']]
dotplot_df_region['Consensus_name'] = [re.sub('_\(.*\)', '', cistrome_name) for cistrome_name in dotplot_df_region['Name']]
# Merge
dotplot_df = pd.merge(dotplot_df_gene, dotplot_df_region, on=['Group', 'Consensus_name'])
dotplot_df.columns = ['TF', 'Group', 'TF_expression_gene', 'Name_gene', 'eRegulon_AUC_gene', 'is_extended_gene', 'Consensus_name', 'TF_region', 'TF_expression_region', 'Name_region', 'eRegulon_AUC_region', 'is_extended_gene']
[35]:
dotplot(dotplot_df,
                     ax = None,
                     region_set_key = 'Name_gene',
                     order_cistromes_by_max = 'eRegulon_AUC_gene',
                     cluster = 'group', #can be group, TF or both
                     color_var = 'eRegulon_AUC_region',
                     size_var = 'eRegulon_AUC_gene',
                     order_group = None,
                     order_cistromes = None,
                     n_clust = 10,
                     min_point_size = 1,
                     max_point_size = 15,
                     cmap = 'viridis',
                     vmin = 0,
                     vmax = 1,
                     x_tick_rotation = 45,
                     x_tick_ha = 'right',
                     fontsize  = 9,
                     z_score_expr = False,
                     z_score_enr = False,
                     grid_color = 'grey',
                     grid_lw = 0.5,
                     highlight = None,
                     highlight_lw = 1,
                     plotly_height=1500,
                     use_plotly = True)

In addition, to assess which eRegulons tend to be enriched in the same group of cells we can generate a correlation plot as well.

[36]:
#from scenicplus.plotting.correlation_plot import *
correlation_heatmap(scplus_obj,
                    auc_key = 'eRegulon_AUC',
                    signature_keys = ['Gene_based'],
                    selected_regulons = selected_cistromes['Cistrome'],
                    fcluster_threshold = 0.1,
                    fontsize = 3)

We can also check the overlap between eRegulons:

[46]:
from scenicplus.plotting.correlation_plot import *
jaccard_heatmap(scplus_obj,
                    gene_or_region_based = 'Gene_based',
                    signature_key = 'eRegulon_signatures',
                    selected_regulons = selected_cistromes['Cistrome'].tolist(),
                    fcluster_threshold = 0.1,
                    fontsize = 3)

We can also binarize the eRegulons as in SCENIC. This information will be used afterwards for generating the loom file.

[21]:
binarize_AUC(scplus_obj,
             auc_key='eRegulon_AUC',
             out_key='eRegulon_AUC_thresholds',
             signature_keys=['Gene_based', 'Region_based'],
             n_cpu=8)
[22]:
import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

C. eGRN dimensionality reduction

We can also use the eRegulons for clustering and visualization. The combination of both gene and region based eRegulons results in a better clustering.

[1]:
import dill
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = dill.load(infile)
infile.close()
[23]:
from scenicplus.dimensionality_reduction import *
run_eRegulons_umap(scplus_obj,
                   scale=True, signature_keys=['Gene_based', 'Region_based'])
run_eRegulons_tsne(scplus_obj,
                   scale=True, signature_keys=['Gene_based', 'Region_based'])

We can compare with the previous annotations based on the analyses of the scRNA-seq layer with VSN (Scanpy) and Seurat.

[24]:
plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP',
                 variables=['ACC_VSN_cell_type', 'ACC_Seurat_cell_type'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)
_images/single_sample_tutorial_116_0.png

We can also compare with using the layers independently as well.

[25]:
run_eRegulons_umap(scplus_obj,
                   scale=True, signature_keys=['Gene_based'],
                   reduction_name='eRegulons_UMAP_gb')
run_eRegulons_tsne(scplus_obj,
                   scale=True, signature_keys=['Gene_based'],
                   reduction_name='eRegulons_tSNE_gb')
run_eRegulons_umap(scplus_obj,
                   scale=True, signature_keys=['Region_based'],
                   reduction_name='eRegulons_UMAP_rb')
run_eRegulons_tsne(scplus_obj,
                   scale=True, signature_keys=['Region_based'],
                   reduction_name='eRegulons_tSNE_rb')
[26]:
plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP_gb',
                 variables=['ACC_VSN_cell_type', 'ACC_Seurat_cell_type'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)
_images/single_sample_tutorial_119_0.png
[27]:
plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP_rb',
                 variables=['ACC_VSN_cell_type', 'ACC_Seurat_cell_type'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)
_images/single_sample_tutorial_120_0.png

In addition, we can also use the eRegulon enrichment for clustering.

[30]:
find_clusters(scplus_obj,
              signature_keys=['Gene_based', 'Region_based'],
              k = 10,
              res = [0.6, 1.2, 1.5],
              prefix = 'SCENIC+_',
              scale = True)
[31]:
plot_metadata(scplus_obj,
                 reduction_name='eRegulons_UMAP',
                 variables=['ACC_VSN_cell_type', 'SCENIC+_leiden_10_1.5'],
                 num_columns=2,
                 text_size=10,
                 dot_size=5)
_images/single_sample_tutorial_123_0.png

We can also plot TF expression and eRegulon enrichment (based on target genes and regions, respectively).

[35]:
# For example, for some OL TFs
plot_eRegulon(scplus_obj,
              reduction_name='eRegulons_UMAP',
              selected_regulons=['PRRX2_+_+','OLIG2_+_+', 'SOX10_+_+', 'TCF12_+_+'],
              normalize_tf_expression=True)
_images/single_sample_tutorial_125_0.png
[37]:
# To show between SOX10 and SOX5 (SOX5 targets and represses SOX10 regions)
plot_eRegulon(scplus_obj,
              reduction_name='eRegulons_UMAP',
              selected_regulons=['SOX10_+_+', 'SOX9_+_+', 'SOX5_-_+', 'SOX6_-_+'],
              normalize_tf_expression=True)
_images/single_sample_tutorial_126_0.png
[ ]:
import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

D. eRegulon specificity scores

Recently, a novel method was developed and used to quantify the specificity of the regulons (from SCENIC) across different cell types (Suo et al., 2018). The RSS (Regulon Specificity Score) does not require binarization of a regulon’s enrichment score distribution and measures the distance between this distribution and the distribution of group annotations using the Jensen–Shannon Divergence. For a given group, the RSS for all predicted regulons is ranked from high to low, and highly group-specific regulons are spotted as outliers. This method can be applied to eRegulons as well.

[ ]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[38]:
from scenicplus.RSS import *
regulon_specificity_scores(scplus_obj,
                         'ACC_Seurat_cell_type',
                         signature_keys=['Gene_based'],
                         selected_regulons=selected_cistromes['Cistrome'],
                         out_key_suffix='_gene_based',
                         scale=False)
[39]:
plot_rss(scplus_obj, 'ACC_Seurat_cell_type_gene_based', num_columns=4, top_n=10)
_images/single_sample_tutorial_132_0.png
[ ]:
import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

E. Integrated multiome plot

We can also generate plots showing the chromatin profiles per group, region-to-gene relationships and TF and gene expression.

[ ]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[40]:
# Generate interaction and annotation pyranges
import matplotlib.pyplot as plt
from scenicplus.utils import get_interaction_pr
import pyranges as pr
bigwig_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/consensus_peak_calling/seurat_pseudobulk_bw_files/'
bw_dict = {x.replace('.bw', ''): os.path.join(bigwig_dir, x) for x in os.listdir(bigwig_dir) if '.bw' in x}
pr_consensus_bed = pr.read_bed('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistopic/consensus_peak_calling/consensus_regions.bed')
pr_interact = get_interaction_pr(scplus_obj, 'hsapiens', 'hg38', inplace = False, subset_for_eRegulons_regions = True, eRegulons_key = 'eRegulons_importance')
gtf_file = "/lustre1/project/stg_00002/lcb/fderop/data/00000000_genomes/GRCh38_STAR_2.7.5_rna/genes.gtf"
pr_gtf = pr.read_gtf(gtf_file)
/opt/venv/lib/python3.8/site-packages/scenicplus/utils.py:655: DtypeWarning:

Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.

/opt/venv/lib/python3.8/site-packages/scenicplus/utils.py:712: RuntimeWarning:

invalid value encountered in true_divide

/opt/venv/lib/python3.8/site-packages/scenicplus/utils.py:723: RuntimeWarning:

invalid value encountered in true_divide

[70]:
# Plot
from importlib import reload
from scenicplus.plotting.coverageplot import *
fig = coverage_plot(
        SCENICPLUS_obj = scplus_obj,
        bw_dict = bw_dict,
        region = 'chr1:225800009-225860175',
        figsize = (10,20),
            pr_gtf = pr_gtf,
        color_dict = None,
        plot_order = None,
        pr_interact = pr_interact,
        genes = ['TMEM63A', 'SOX5'],
        meta_data_key = 'ACC_Seurat_cell_type',
        pr_consensus_bed = pr_consensus_bed,
        fontsize_dict={'bigwig_label': 12, 'gene_label': 0, 'violinplots_xlabel': 10, 'title': 12, 'bigwig_tick_label': 0, 'violinplots_ylabel': 3},
        height_ratios_dict = {'bigwig_violin': 1, 'genes': 0.5, 'arcs': 10, 'custom_ax': 5})
plt.tight_layout()
_images/single_sample_tutorial_138_0.png
[71]:
import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

F. Adding DEGs and DARs

We can as well include DEGs and DARs in the SCENIC+ object. Both DEGs and DARs can be computed; however, we recommend to compute DARs with pycisTopic as it provides a more efficient implementation for big data sets (and pycisTopic DARs are already used for motif enrichment). Note that results can be slightly different due to filtering.

[1]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[6]:
#from pyscenic.diff_features import *
get_differential_features(scplus_obj, 'ACC_VSN_cell_type', use_hvg = True, contrast_type = ['DARs', 'DEGs'])
get_differential_features(scplus_obj, 'ACC_Seurat_cell_type', use_hvg = True, contrast_type = ['DARs', 'DEGs'])
2022-01-05 19:49:40,968 SCENIC+      INFO     Calculating DARs for variable ACC_VSN_cell_type
2022-01-05 19:50:13,849 SCENIC+      INFO     There are 93456 variable features
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical
2022-01-05 19:51:25,668 SCENIC+      INFO     Finished calculating DARs for variable ACC_VSN_cell_type
2022-01-05 19:51:25,671 SCENIC+      INFO     Calculating DEGs for variable ACC_VSN_cell_type
2022-01-05 19:51:31,555 SCENIC+      INFO     There are 4134 variable features
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical
2022-01-05 19:51:34,598 SCENIC+      INFO     Finished calculating DEGs for variable ACC_VSN_cell_type
2022-01-05 19:51:34,600 SCENIC+      INFO     Calculating DARs for variable ACC_Seurat_cell_type
2022-01-05 19:52:08,736 SCENIC+      INFO     There are 101191 variable features
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical
2022-01-05 19:53:36,129 SCENIC+      INFO     Finished calculating DARs for variable ACC_Seurat_cell_type
2022-01-05 19:53:36,132 SCENIC+      INFO     Calculating DEGs for variable ACC_Seurat_cell_type
2022-01-05 19:53:37,694 SCENIC+      INFO     There are 4524 variable features
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'GEX_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.9' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_leiden_res0.3' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_sample_id' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_leiden_res1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_RNA+ATAC_leiden_100_2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_harmony_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_bbknn_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_scanorama_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_pycisTopic_cca_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_0.6' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.2' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
Trying to set attribute `.obs` of view, copying.
... storing 'SCENIC+_leiden_10_1.5' as categorical
2022-01-05 19:53:41,195 SCENIC+      INFO     Finished calculating DEGs for variable ACC_Seurat_cell_type
[18]:
import dill
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'wb') as f:
  dill.dump(scplus_obj, f)

G. Export to loom

You can also export the data to a loom file (gene based and region based, respectively). These loom files can be queried at https://scope.aertslab.org/.

[1]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[3]:
from scenicplus.loom import *
export_to_loom(scplus_obj,
               signature_key = 'Gene_based',
               tree_structure = ('10x_multiome_brain', 'SCENIC+'),
               title = 'Tutorial - Gene based eGRN',
               nomenclature = "hg38",
               out_fname='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/HC_gene_based.loom')
2022-01-06 17:37:54,023 SCENIC+      INFO     Formatting data
2022-01-06 17:38:01,938 SCENIC+      INFO     Creating minimal loom
2022-01-06 17:38:13,182 SCENIC+      INFO     Adding annotations
2022-01-06 17:38:16,020 SCENIC+      INFO     Adding clusterings
2022-01-06 17:38:16,375 SCENIC+      INFO     Adding markers
2022-01-06 17:38:17,649 SCENIC+      INFO     Exporting
/opt/venv/lib/python3.8/site-packages/loomxpy/loomxpy.py:459: FutureWarning: The default value of regex will change from True to False in a future version.
  regulons.columns = regulons.columns.str.replace("_?\\(", "_(")
/opt/venv/lib/python3.8/site-packages/loomxpy/loomxpy.py:437: FutureWarning: The default value of regex will change from True to False in a future version.
  regulons.columns = regulons.columns.str.replace("_?\\(", "_(")
[5]:
export_to_loom(scplus_obj,
               signature_key = 'Region_based',
               tree_structure = ('10x_multiome_brain', 'SCENIC+'),
               title = 'Tutorial - Gene based eGRN',
               nomenclature = "hg38",
               out_fname='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/HC_region_based.loom')
2022-01-06 17:38:44,989 SCENIC+      INFO     Formatting data
2022-01-06 17:38:59,521 SCENIC+      INFO     Creating minimal loom
2022-01-06 17:43:59,040 SCENIC+      INFO     Adding annotations
2022-01-06 17:44:49,492 SCENIC+      INFO     Adding clusterings
2022-01-06 17:44:49,952 SCENIC+      INFO     Adding markers
2022-01-06 17:45:17,312 SCENIC+      INFO     Exporting

G. cisTarget on eRegulons

You can confirm the quality of the eRegulons by running cisTarget on them.

[12]:
import pyranges as pr
from pycistarget.utils import *
selected_regulons = ['SOX10_+_+', 'OLIG2_+_+']
selected_regulons = [list(filter(lambda x: x.startswith(y), scplus_obj.uns['eRegulon_signatures']['Region_based'].keys()))[0] for y in selected_regulons]
region_sets = {key: pr.PyRanges(region_names_to_coordinates(scplus_obj.uns['eRegulon_signatures']['Region_based'][key])) for key in selected_regulons}
selected_regulons
[12]:
['SOX10_+_+_(614r)', 'OLIG2_+_+_(172r)']
[13]:
# Load cistarget functions
from pycistarget.motif_enrichment_cistarget import *
# Preload db, you can also just provide the path to the db. Preloading the database is useful if you want to test different parameters.
# This will take some time depending on the size of your database
db = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/atac/pycistarget/dbs/human_brain.regions_vs_motifs.rankings.feather'
ctx_db = cisTargetDatabase(db, region_sets)
# Remove dbcorr motifs
ctx_db.db_rankings = ctx_db.db_rankings[~ctx_db.db_rankings.index.str.contains("dbcorr")]
# Run
cistarget_dict = run_cistarget(ctx_db = ctx_db,
                               region_sets = region_sets,
                               specie = 'homo_sapiens',
                               annotation_version = 'v9nr_clust',
                               path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/motif_clustering/RNA_harmony_snn_res_5_clusters/motif_collection_combined_motifs_stamp_and_singlets/hs_annotation.tsv',
                               auc_threshold = 0.005,
                               nes_threshold = 3.0,
                               rank_threshold = 0.05,
                               annotation = ['Direct_annot', 'Orthology_annot'],
                               n_cpu = 1,
                               _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')
2022-01-06 17:53:14,926 cisTarget    INFO     Reading cisTarget database
2022-01-06 17:54:11,634 cisTarget    INFO     Running cisTarget for SOX10_+_+_(614r) which has 614 regions
2022-01-06 17:54:38,745 cisTarget    INFO     Annotating motifs for SOX10_+_+_(614r)
2022-01-06 17:54:40,659 cisTarget    INFO     Getting cistromes for SOX10_+_+_(614r)
2022-01-06 17:54:40,948 cisTarget    INFO     Running cisTarget for OLIG2_+_+_(172r) which has 172 regions
2022-01-06 17:54:46,872 cisTarget    INFO     Annotating motifs for OLIG2_+_+_(172r)
2022-01-06 17:54:49,364 cisTarget    INFO     Getting cistromes for OLIG2_+_+_(172r)
2022-01-06 17:54:49,890 cisTarget    INFO     Done!
[16]:
cistarget_results(cistarget_dict, name='SOX10_+_+_(614r)')
[16]:
Logo Region_set Direct_annot Orthology_annot NES AUC Rank_at_max Motif_hits
metacluster_46.3 SOX10_+_+_(614r) SOX10, SOX8 NaN 14.464807 0.039040 21594.0 194
homer__CCATTGTTNY_Sox6 SOX10_+_+_(614r) NaN SOX6 13.828971 0.037454 21665.0 167
hocomoco__SOX9_HUMAN.H11MO.0.B SOX10_+_+_(614r) SOX9 NaN 11.155584 0.030785 21704.0 128
metacluster_46.1 SOX10_+_+_(614r) HBP1, SOX13, SRY, SOX7, SOX10, SOX21, SOX30, SOX9, SOX2, SOX3, SOX15, SOX6, SOX4, SOX17, SOX1, SOX11, SOX18, SOX5, SOX8 SOX21, SOX17, SOX14 10.883511 0.030107 21232.0 204
taipale_cyt_meth__SOX14_NGAACAATGN_eDBD_meth SOX10_+_+_(614r) NaN NaN 10.849951 0.030023 21490.0 147
metacluster_46.17 SOX10_+_+_(614r) SOX10, SOX21, SOX6, SOX4, SOX12, SOX18, SRY, SMAD1, SOX14, SOX9, SOX5, SOX13, SOX2, SOX3, SOX15, SOX11, SOX8 SOX10, SOX6, SOX4, FOXJ3, SOX2, SOX3 10.448134 0.029021 21568.0 200
cisbp__M5208 SOX10_+_+_(614r) NaN SOX7, SOX17, SOX18 10.072985 0.028085 21647.0 157
transfac_pro__M08972 SOX10_+_+_(614r) SOX17 NaN 9.528539 0.026727 21635.0 172
metacluster_46.18 SOX10_+_+_(614r) SOX6, NANOG, SOX30, SRY, SOX18, SOX14, SOX9, SOX17, SOX13, SOX5, SOX2, SOX3, SOX15, SOX8 SOX21, SOX30, SOX12, SOX18, SRY, SOX14, SOX17, SOX2, NANOGP8, SOX7, SOX15, SOX11 9.178860 0.025855 21716.0 176
metacluster_46.2 SOX10_+_+_(614r) SOX10, SOX7, SRY, SOX17 NaN 8.961321 0.025312 21788.0 161
swissregulon__hs__SOX2.p2 SOX10_+_+_(614r) SOX2 NaN 8.960123 0.025309 21559.0 147
transfac_pro__M01308 SOX10_+_+_(614r) SOX4 NaN 8.839967 0.025009 21618.0 156
factorbook__SOX2 SOX10_+_+_(614r) SOX2 NaN 8.830678 0.024986 21593.0 144
jaspar__MA0515.1 SOX10_+_+_(614r) NaN SOX6 8.796220 0.024900 20253.0 145
cisbp__M5207 SOX10_+_+_(614r) NaN SOX4, SOX12, SOX11 8.790526 0.024886 21566.0 116
cisbp__M1904 SOX10_+_+_(614r) SOX9 NaN 8.548118 0.024281 21785.0 170
metacluster_46.26 SOX10_+_+_(614r) NaN NaN 8.296420 0.023653 21589.0 106
taipale_cyt_meth__SOX14_CCGAACAATN_FL_meth SOX10_+_+_(614r) SOX14 NaN 7.757967 0.022310 20657.0 95
metacluster_2.93 SOX10_+_+_(614r) SOX9, SOX17, SOX4, SOX18 NaN 7.619833 0.021966 21638.0 165
swissregulon__hs__SOX_8_9_10_.p2 SOX10_+_+_(614r) SOX10, SOX9, SOX8 NaN 7.029543 0.020493 21716.0 168
cisbp__M1601 SOX10_+_+_(614r) NaN SOX11 6.980402 0.020371 21654.0 146
metacluster_1.73 SOX10_+_+_(614r) SOX5, SOX6 NaN 6.814102 0.019956 21447.0 142
transfac_pro__M07338 SOX10_+_+_(614r) SOX10 NaN 6.533939 0.019257 21758.0 185
transfac_pro__M07286 SOX10_+_+_(614r) FOXO1 NaN 6.351158 0.018801 21762.0 73
hocomoco__SOX4_HUMAN.H11MO.0.B SOX10_+_+_(614r) SOX4 NaN 6.314602 0.018710 21406.0 141
metacluster_46.22 SOX10_+_+_(614r) SOX17 SOX4, SOX30, SOX12, SOX5, SRY, SOX8 6.298721 0.018670 20652.0 144
metacluster_1.71 SOX10_+_+_(614r) NaN ZNF8 6.178865 0.018371 19245.0 65
hocomoco__SOX2_HUMAN.H11MO.0.A SOX10_+_+_(614r) SOX2 NaN 6.174371 0.018360 21633.0 138
swissregulon__hs__SRY.p2 SOX10_+_+_(614r) SRY NaN 6.127927 0.018244 20473.0 107
metacluster_61.1 SOX10_+_+_(614r) POLR2A, SOX3 SOX14 6.032042 0.018005 20289.0 109
tfdimers__MD00293 SOX10_+_+_(614r) SMAD1, SOX4 NaN 5.987995 0.017895 20532.0 122
metacluster_1.72 SOX10_+_+_(614r) SRY IRX3, IRX5, IRX1, IRX2, IRX4, IRX6 5.949341 0.017799 21253.0 102
metacluster_1.13 SOX10_+_+_(614r) FOXC2, FOXD3, SOX4 NaN 5.763864 0.017336 12447.0 46
transfac_public__M00042 SOX10_+_+_(614r) SOX5 NaN 5.641911 0.017032 21689.0 136
transfac_pro__M01630 SOX10_+_+_(614r) NaN FOXI2, FOXI3, FOXG1, FOXK1, FOXJ2, FOXF1, FOXJ3, FOXF2, FOXK2, FOXJ1, FOXL2, FOXI1, FOXQ1 5.585578 0.016891 19643.0 76
metacluster_46.21 SOX10_+_+_(614r) SOX12, SOX14 NaN 5.553817 0.016812 21686.0 111
transfac_pro__M01216 SOX10_+_+_(614r) FOXO1, FOXG1 NaN 5.346166 0.016294 18303.0 71
metacluster_49.30 SOX10_+_+_(614r) SOX5 SOX5, SOX13 5.303318 0.016187 20082.0 116
transfac_public__M00410 SOX10_+_+_(614r) SOX9 NaN 5.267960 0.016099 21727.0 143
jaspar__MA0143.3 SOX10_+_+_(614r) NaN NaN 5.261668 0.016083 21762.0 107
metacluster_22.14 SOX10_+_+_(614r) NaN IRX3, IRX5, DMRTA2, IRX1, IRX2, DMRTA1, IRX4, IRX6 5.132823 0.015762 8711.0 48
cisbp__M5110 SOX10_+_+_(614r) NaN IRX3, IRX5, IRX1, IRX2, IRX4, IRX6 5.125931 0.015745 12395.0 50
transfac_public__M00148 SOX10_+_+_(614r) SRY NaN 5.093869 0.015665 21362.0 76
metacluster_46.15 SOX10_+_+_(614r) NANOG, TCF7L1, POU5F1, SOX2 NaN 5.032144 0.015511 20843.0 91
tfdimers__MD00291 SOX10_+_+_(614r) YY2, FOXO1 NaN 5.028248 0.015501 21293.0 92
hdpi__BOLL SOX10_+_+_(614r) BOLL NaN 5.013266 0.015464 21176.0 68
predrem__nrMotif1 SOX10_+_+_(614r) NaN NaN 4.799323 0.014930 19284.0 62
cisbp__M2502 SOX10_+_+_(614r) NaN NaN 4.735200 0.014770 10556.0 48
metacluster_23.20 SOX10_+_+_(614r) SRY METTL14 4.734601 0.014769 20641.0 69
metacluster_2.54 SOX10_+_+_(614r) SOX10, DMRT1 DMRTB1, HBP1 4.696247 0.014673 21777.0 153
hocomoco__SOX2_HUMAN.H11MO.1.A SOX10_+_+_(614r) SOX2 NaN 4.683063 0.014640 20853.0 79
metacluster_46.8 SOX10_+_+_(614r) SOX9, HNF4G, SOX15 SOX12, SOX11, SOX4 4.614446 0.014469 19952.0 103
taipale_tf_pairs__SOX6_CACCGAACAAT_HT SOX10_+_+_(614r) SOX6 NaN 4.542832 0.014290 15747.0 76
hocomoco__SOX9_MOUSE.H11MO.1.A SOX10_+_+_(614r) NaN SOX9 4.542232 0.014289 21368.0 134
cisbp__M6017 SOX10_+_+_(614r) NaN FOXJ3 4.535341 0.014272 1646.0 15
metacluster_1.9 SOX10_+_+_(614r) FOXO4, FOXP2, FOXO1 FOXI2, FOXI3, FOXG1, FOXK1, FOXJ2, FOXF1, FOXJ3, FOXF2, FOXO1, FOXJ1, FOXK2, FOXL2, FOXI1, FOXQ1 4.502380 0.014189 18273.0 65
hocomoco__FOXO4_HUMAN.H11MO.0.C SOX10_+_+_(614r) FOXO4 NaN 4.445149 0.014047 17178.0 63
metacluster_86.5 SOX10_+_+_(614r) NR3C1, AR, PGR AR, PGR 4.347466 0.013803 6196.0 32
taipale__SOX21_DBD_AACAATNNNNAKTGTT SOX10_+_+_(614r) SOX21 NaN 4.257574 0.013579 13472.0 52
transfac_pro__M02807 SOX10_+_+_(614r) NaN NaN 4.221618 0.013489 21720.0 109
cisbp__M1597 SOX10_+_+_(614r) NaN SOX14 4.205137 0.013448 21749.0 114
transfac_pro__M02907 SOX10_+_+_(614r) NaN SOX21 4.117043 0.013228 21626.0 113
metacluster_46.16 SOX10_+_+_(614r) NaN NaN 4.001083 0.012939 3326.0 20
metacluster_86.15 SOX10_+_+_(614r) NR3C1, AR, PGR NaN 3.976512 0.012878 1423.0 12
cisbp__M0749 SOX10_+_+_(614r) NaN FOXI2, FOXI3, FOXG1, FOXK1, FOXJ2, FOXF1, FOXJ3, FOXF2, FOXK2, FOXJ1, FOXL2, FOXI1, FOXQ1 3.920779 0.012739 21117.0 67
tfdimers__MD00591 SOX10_+_+_(614r) SRY, TFAP2C NaN 3.895609 0.012676 18111.0 78
metacluster_9.78 SOX10_+_+_(614r) NaN NaN 3.894411 0.012673 18643.0 66
transfac_pro__M01082 SOX10_+_+_(614r) BRCA1, USF2 NaN 3.873736 0.012621 21629.0 76
metacluster_83.10 SOX10_+_+_(614r) NaN NaN 3.772158 0.012368 7670.0 30
metacluster_46.19 SOX10_+_+_(614r) NaN SOX5 3.770959 0.012365 21533.0 114
metacluster_48.24 SOX10_+_+_(614r) HOXD10 NaN 3.753880 0.012322 1243.0 11
metacluster_6.40 SOX10_+_+_(614r) TOB2 NaN 3.729010 0.012260 2119.0 13
transfac_pro__M01146 SOX10_+_+_(614r) DMRT1 NaN 3.686161 0.012153 15072.0 53
cisbp__M2132 SOX10_+_+_(614r) NaN NaN 3.659194 0.012086 21175.0 71
predrem__nrMotif1554 SOX10_+_+_(614r) NaN NaN 3.600165 0.011939 8867.0 35
taipale_tf_pairs__SOX17_ACCGAACAAT_HT SOX10_+_+_(614r) SOX17 NaN 3.512969 0.011721 21229.0 88
flyfactorsurvey__BCl6-F5_CG4360F2-3_SOLEXA_2.5 SOX10_+_+_(614r) NaN NaN 3.510872 0.011716 1067.0 10
taipale_cyt_meth__SOX12_ACCGAACAATN_eDBD_meth SOX10_+_+_(614r) NaN NaN 3.496190 0.011679 21730.0 99
cisbp__M0720 SOX10_+_+_(614r) NaN NaN 3.395810 0.011429 19112.0 55
metacluster_23.5 SOX10_+_+_(614r) ABL1, BPTF, FOXP1, FUBP1 FOXP2, FOXL1, FOXP4, FOXP3, FOXP1, FOXM1 3.385323 0.011403 16948.0 57
metacluster_2.61 SOX10_+_+_(614r) ACO1 NaN 3.363149 0.011348 1740.0 13
metacluster_14.62 SOX10_+_+_(614r) NaN NaN 3.348467 0.011311 1550.0 12
metacluster_23.3 SOX10_+_+_(614r) ZIM3 NaN 3.312810 0.011222 21165.0 60
metacluster_14.21 SOX10_+_+_(614r) SOX18 NaN 3.284644 0.011152 19148.0 68
metacluster_19.39 SOX10_+_+_(614r) NNT NaN 3.265467 0.011104 956.0 9
metacluster_46.10 SOX10_+_+_(614r) POU5F1, CBX3, TAF7, SOX9, TCF12, NR3C1 NaN 3.264568 0.011102 21614.0 87
taipale_tf_pairs__HOXB2_SOX15_NYMATTANNNNNNACAATR_CAP_repr SOX10_+_+_(614r) HOXB2, SOX15 NaN 3.245091 0.011053 21491.0 79
metacluster_23.31 SOX10_+_+_(614r) NaN NaN 3.230709 0.011017 8906.0 34
cisbp__M6261 SOX10_+_+_(614r) NR3C1 NaN 3.219922 0.010990 8191.0 30
metacluster_1.8 SOX10_+_+_(614r) FOXP2, FOXO4, FOXP1, FOXO1, FOXO6, FOXO3, FOXQ1 FOXP2, FOXP1, FOXJ3, FOXO1, FOXJ1, FOXL2, FOXP4, FOXP3, FOXO3 3.210932 0.010968 21728.0 61
tfdimers__MD00100 SOX10_+_+_(614r) SOX10, FOXO1, FOXG1 NaN 3.192055 0.010921 14149.0 44
metacluster_78.18 SOX10_+_+_(614r) NaN NaN 3.151004 0.010818 1909.0 12
predrem__nrMotif1377 SOX10_+_+_(614r) NaN NaN 3.131528 0.010770 11317.0 41
hocomoco__ANDR_HUMAN.H11MO.2.A SOX10_+_+_(614r) AR NaN 3.123438 0.010750 20311.0 56
transfac_pro__M03193 SOX10_+_+_(614r) NaN NaN 3.118943 0.010738 1236.0 9
metacluster_46.35 SOX10_+_+_(614r) NaN NaN 3.096170 0.010682 21769.0 83
metacluster_1.11 SOX10_+_+_(614r) FOXC1 NaN 3.059914 0.010591 1979.0 12
metacluster_49.24 SOX10_+_+_(614r) NaN NaN 3.053621 0.010575 613.0 7
cisbp__M6033 SOX10_+_+_(614r) NaN HOXD3 3.024257 0.010502 1388.0 10
metacluster_49.28 SOX10_+_+_(614r) HOXC8, FOXD2 IRX3, FOXD2, IRX5, FOXD4L3, FOXD4L4, FOXD4L5, FOXD1, FOXD4L1, FOXD4L6, IRX1, FOXD3, IRX2, IRX4, IRX6, FOXD4 3.015567 0.010481 2587.0 13

H. Plotting networks

We can also plot networks using networkx and pyvis (for interactive networks). For example, here we are going to make a small network with 10 oligodendroycte genes controlled by SOX10, OLIG2 and TCF12.

[1]:
from scenicplus.networks import *
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()

The following function will generate a dictionary with node and edge features. It is possible to add additional columns to these data frames (e.g. enrichment of a motif in a region), to use it afterwards as variable to control the features of nodes and edges when plotting. Alternatively, we can also calculate logFC between given groups to use as variable as well.

[2]:
import networkx as nx
subset_genes = ['PLP1', 'SOX8', 'EPN1', 'OLIG1', 'MOG', 'OLIG2', 'SOX10', 'PLLP', 'PBX3', 'LRRN1', 'UBL8']
nx_tables = create_nx_tables(scplus_obj,
                    eRegulon_metadata_key = 'eRegulon_metadata',
                    subset_eRegulons = ['SOX10', 'OLIG2', 'TCF12'],
                    subset_regions = None,
                    subset_genes = subset_genes,
                    add_differential_gene_expression = True,
                    add_differential_region_accessibility = True,
                    differential_variable = ['ACC_Seurat_cell_type', 'ACC_VSN_cell_type'])
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_Seurat_cell_type' as categorical
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1228: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.
  c.reorder_categories(natsorted(c.categories), inplace=True)
/opt/venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1236: ImplicitModificationWarning: Initializing view as actual.
  warnings.warn(
Trying to set attribute `.obs` of view, copying.
... storing 'ACC_VSN_cell_type' as categorical

We can now generate the network. To control features (e.g. size, color and transparency of nodes; width, color, transparency of edges, size and color of labels), we can pass dictionaries. Each dictitonary will be named by the slot on the tables dictionary and will include the name of the variable to use (‘variable’; if you want to use a fixed value use ‘fixed_VARNAME’), and scales (a dictionary if a discrete variable is used, a colormap if a continuous map is used, with minimum and maximum values). Two layouts are available: ‘concentrical’ and Kamada Kawai.

[3]:
from scenicplus.networks import *
G_c, pos_c, edge_tables_c, node_tables_c = create_nx_graph(nx_tables,
                   use_edge_tables = ['TF2R','R2G'],
                   color_edge_by = {'TF2R': {'variable' : 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple', 'TCF12': 'Red'}},
                                    'R2G': {'variable' : 'R2G_rho', 'continuous_color' : 'viridis', 'v_min': -1, 'v_max': 1}},
                   transparency_edge_by =  {'R2G': {'variable' : 'R2G_importance', 'min_alpha': 0.1, 'v_min': 0}},
                   width_edge_by = {'R2G': {'variable' : 'R2G_importance', 'max_size' :  1.5, 'min_size' : 1}},
                   color_node_by = {'TF': {'variable': 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple',  'TCF12': 'Red'}},
                                    'Gene': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'bwr'},
                                    'Region': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'viridis'}},
                   transparency_node_by =  {'Region': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1},
                                    'Gene': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1}},
                   size_node_by = {'TF': {'variable': 'fixed_size', 'fixed_size': 30},
                                    'Gene': {'variable': 'fixed_size', 'fixed_size': 15},
                                    'Region': {'variable': 'fixed_size', 'fixed_size': 10}},
                   shape_node_by = {'TF': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Gene': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Region': {'variable': 'fixed_shape', 'fixed_shape': 'diamond'}},
                   label_size_by = {'TF': {'variable': 'fixed_label_size', 'fixed_label_size': 20.0},
                                    'Gene': {'variable': 'fixed_label_size', 'fixed_label_size': 10.0},
                                    'Region': {'variable': 'fixed_label_size', 'fixed_label_size': 0.0}},
                   layout='concentrical_layout',
                   scale_position_by=250)

It is possible to plot static networks with networkx:

[4]:
# To draw with networkx
plt.figure(figsize=(10,7))
plot_networkx(G_c, pos_c)
_images/single_sample_tutorial_163_0.png

Or to generate an interactive visualization with pyvis. If opening the html file, you will see a menu to change options:

[5]:
# Visualize with pyvis
import os
os.chdir('/data/leuven/313/vsc31305/jupyterhub_notebooks/Multiomics_pipeline/10x_human_cerebellum/')
from pyvis.network import Network
nt = Network(notebook=True)
nt.from_nx(G_c)
nt.show_buttons()
nt.toggle_physics(False)
nt.show('olig_small_nx_c.html')
[5]:
[6]:
# To save as html
from pyvis.network import Network
nt = Network(notebook=False)
nt.from_nx(G_c)
nt.show_buttons(filter_=['physics'])
nt.toggle_physics(False)
nt.show('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/olig_small_nx_c.html')

We can also show the results using the Kamada Kawai algorithm:

[7]:
G_kk, pos_kk, edge_tables_kk, node_tables_kk = create_nx_graph(nx_tables,
                   use_edge_tables = ['TF2R','R2G'],
                   color_edge_by = {'TF2R': {'variable' : 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple', 'TCF12': 'Red'}},
                                    'R2G': {'variable' : 'R2G_rho', 'continuous_color' : 'viridis', 'v_min': -1, 'v_max': 1}},
                   transparency_edge_by =  {'R2G': {'variable' : 'R2G_importance', 'min_alpha': 0.1, 'v_min': 0}},
                   width_edge_by = {'R2G': {'variable' : 'R2G_importance', 'max_size' :  1.5, 'min_size' : 1}},
                   color_node_by = {'TF': {'variable': 'TF', 'category_color' : {'SOX10': 'Orange', 'OLIG2': 'Purple',  'TCF12': 'Red'}},
                                    'Gene': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'bwr'},
                                    'Region': {'variable': 'ACC_Seurat_cell_type_Log2FC_MOL', 'continuous_color' : 'viridis'}},
                   transparency_node_by =  {'Region': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1},
                                    'Gene': {'variable' : 'ACC_Seurat_cell_type_Log2FC_MOL', 'min_alpha': 0.1}},
                   size_node_by = {'TF': {'variable': 'fixed_size', 'fixed_size': 30},
                                    'Gene': {'variable': 'fixed_size', 'fixed_size': 15},
                                    'Region': {'variable': 'fixed_size', 'fixed_size': 10}},
                   shape_node_by = {'TF': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Gene': {'variable': 'fixed_shape', 'fixed_shape': 'ellipse'},
                                    'Region': {'variable': 'fixed_shape', 'fixed_shape': 'diamond'}},
                   label_size_by = {'TF': {'variable': 'fixed_label_size', 'fixed_label_size': 20.0},
                                    'Gene': {'variable': 'fixed_label_size', 'fixed_label_size': 10.0},
                                    'Region': {'variable': 'fixed_label_size', 'fixed_label_size': 0.0}},
                   layout='kamada_kawai_layout',
                   scale_position_by = 500)
[8]:
plt.figure(figsize=(10,10))
plot_networkx(G_kk, pos_kk)
_images/single_sample_tutorial_169_0.png
[9]:
# Visualize with pyvis
import os
os.chdir('/data/leuven/313/vsc31305/jupyterhub_notebooks/Multiomics_pipeline/10x_human_cerebellum/')
from pyvis.network import Network
nt = Network(notebook=True)
nt.from_nx(G_kk)
nt.show_buttons()
nt.toggle_physics(False)
nt.show('olig_small_nx_kk.html')
[9]:
[10]:
# To save as html
from pyvis.network import Network
nt = Network(notebook=False)
nt.from_nx(G_kk)
nt.show_buttons(filter_=['physics'])
nt.toggle_physics(False)
nt.show('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/olig_small_nx_kk.html')

I. Exporting to UCSC

While pseudobulk profiles can be generated with pycisTopic; we can export region eRegulons and region-to-gene links to visualize in UCSC.

[11]:
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/scplus_obj.pkl', 'rb')
scplus_obj = pickle.load(infile)
infile.close()
[12]:
from scenicplus.enhancer_to_gene import export_to_UCSC_interact
r2g_data = export_to_UCSC_interact(scplus_obj,
                            'hsapiens',
                            '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.bed',
                            path_bedToBigBed='/staging/leuven/stg_00002/lcb/sdewin/PhD/De_Winter_hNTorg/COMBINED_ANALYSIS/r2g/',
                            bigbed_outfile='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.interact.bb',
                            region_to_gene_key='region_to_gene',
                            pbm_host='http://www.ensembl.org',
                            assembly='hg38',
                            ucsc_track_name='HC_R2G',
                            ucsc_description='Region_to_gene in the human cerebellum 10X data set',
                            cmap_neg='Reds',
                            cmap_pos='Greens',
                            key_for_color='rho',
                            vmin=-1,
                            vmax=1,
                            scale_by_gene=False,
                            subset_for_eRegulons_regions=True,
                            eRegulons_key='eRegulons_importance')
2022-01-11 20:02:09,140 R2G          INFO     Downloading gene annotation from biomart, using dataset: hsapiens_gene_ensembl
2022-01-11 20:02:09,742 R2G          INFO     Formatting data ...
/opt/venv/lib/python3.8/site-packages/scenicplus/enhancer_to_gene.py:750: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.
  annot = dataset.query(attributes=['chromosome_name', 'start_position', 'end_position',
2022-01-11 20:02:30,715 R2G          INFO     Writing data to: /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.bed
2022-01-11 20:02:31,849 R2G          INFO     Writing data to: /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_r2g.rho.interact.bb
[3]:
from scenicplus.utils import export_eRegulons
regions = export_eRegulons(scplus_obj,
                '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_eRegulons.bed',
                'hg38',
                bigbed_outfile = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/analysis/10x_multiome_brain/output/scenicplus/human_cerebellum_eRegulons.bb',
                eRegulon_metadata_key = 'eRegulon_metadata',
                eRegulon_signature_key = 'eRegulon_signatures',
                path_bedToBigBed='/staging/leuven/stg_00002/lcb/sdewin/PhD/De_Winter_hNTorg/COMBINED_ANALYSIS/r2g/')